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object detection</title> <meta name="description" content="Search results for: moving object detection"> <meta name="keywords" content="moving object detection"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="moving object detection"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 5383</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: moving object detection</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5383</span> A Background Subtraction Based Moving Object Detection Around the Host Vehicle</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyojin%20Lim">Hyojin Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Cuong%20Nguyen%20Khac"> Cuong Nguyen Khac</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Youl%20Jung"> Ho-Youl Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose moving object detection method which is helpful for driver to safely take his/her car out of parking lot. When moving objects such as motorbikes, pedestrians, the other cars and some obstacles are detected at the rear-side of host vehicle, the proposed algorithm can provide to driver warning. We assume that the host vehicle is just before departure. Gaussian Mixture Model (GMM) based background subtraction is basically applied. Pre-processing such as smoothing and post-processing as morphological filtering are added.We examine “which color space has better performance for detection of moving objects?” Three color spaces including RGB, YCbCr, and Y are applied and compared, in terms of detection rate. Through simulation, we prove that RGB space is more suitable for moving object detection based on background subtraction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gaussian%20mixture%20model" title="gaussian mixture model">gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20subtraction" title=" background subtraction"> background subtraction</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection" title=" moving object detection"> moving object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=color%20space" title=" color space"> color space</a>, <a href="https://publications.waset.org/abstracts/search?q=morphological%20filtering" title=" morphological filtering"> morphological filtering</a> </p> <a href="https://publications.waset.org/abstracts/32650/a-background-subtraction-based-moving-object-detection-around-the-host-vehicle" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32650.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">617</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5382</span> An Efficient Fundamental Matrix Estimation for Moving Object Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yeongyu%20Choi">Yeongyu Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20H.%20Park"> Ju H. Park</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20M.%20Lee"> S. M. Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Youl%20Jung"> Ho-Youl Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an improved method for estimating fundamental matrix is proposed. The method is applied effectively to monocular camera based moving object detection. The method consists of corner points detection, moving object’s motion estimation and fundamental matrix calculation. The corner points are obtained by using Harris corner detector, motions of moving objects is calculated from pyramidal Lucas-Kanade optical flow algorithm. Through epipolar geometry analysis using RANSAC, the fundamental matrix is calculated. In this method, we have improved the performances of moving object detection by using two threshold values that determine inlier or outlier. Through the simulations, we compare the performances with varying the two threshold values. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corner%20detection" title="corner detection">corner detection</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20flow" title=" optical flow"> optical flow</a>, <a href="https://publications.waset.org/abstracts/search?q=epipolar%20geometry" title=" epipolar geometry"> epipolar geometry</a>, <a href="https://publications.waset.org/abstracts/search?q=RANSAC" title=" RANSAC"> RANSAC</a> </p> <a href="https://publications.waset.org/abstracts/79103/an-efficient-fundamental-matrix-estimation-for-moving-object-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79103.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">409</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5381</span> A Real-Time Moving Object Detection and Tracking Scheme and Its Implementation for Video Surveillance System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mulugeta%20K.%20Tefera">Mulugeta K. Tefera</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaolong%20Yang"> Xiaolong Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian%20Liu"> Jian Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=background%20modeling" title="background modeling">background modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a>, <a href="https://publications.waset.org/abstracts/search?q=inter-frame%20difference" title=" inter-frame difference"> inter-frame difference</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection%20and%20tracking" title=" object detection and tracking"> object detection and tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a> </p> <a href="https://publications.waset.org/abstracts/78578/a-real-time-moving-object-detection-and-tracking-scheme-and-its-implementation-for-video-surveillance-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/78578.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">477</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5380</span> Dynamic Background Updating for Lightweight Moving Object Detection </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kelemewerk%20Destalem">Kelemewerk Destalem</a>, <a href="https://publications.waset.org/abstracts/search?q=Joongjae%20Cho"> Joongjae Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaeseong%20Lee"> Jaeseong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20H.%20Park"> Ju H. Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Joonhyuk%20Yoo"> Joonhyuk Yoo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background subtraction and temporal difference are often used for moving object detection in video. Both approaches are computationally simple and easy to be deployed in real-time image processing. However, while the background subtraction is highly sensitive to dynamic background and illumination changes, the temporal difference approach is poor at extracting relevant pixels of the moving object and at detecting the stopped or slowly moving objects in the scene. In this paper, we propose a moving object detection scheme based on adaptive background subtraction and temporal difference exploiting dynamic background updates. The proposed technique consists of a histogram equalization, a linear combination of background and temporal difference, followed by the novel frame-based and pixel-based background updating techniques. Finally, morphological operations are applied to the output images. Experimental results show that the proposed algorithm can solve the drawbacks of both background subtraction and temporal difference methods and can provide better performance than that of each method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=background%20subtraction" title="background subtraction">background subtraction</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20updating" title=" background updating"> background updating</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time" title=" real time"> real time</a>, <a href="https://publications.waset.org/abstracts/search?q=light%20weight%20algorithm" title=" light weight algorithm"> light weight algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20difference" title=" temporal difference"> temporal difference</a> </p> <a href="https://publications.waset.org/abstracts/31063/dynamic-background-updating-for-lightweight-moving-object-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31063.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">342</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5379</span> Moving Object Detection Using Histogram of Uniformly Oriented Gradient</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wei-Jong%20Yang">Wei-Jong Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu-Siang%20Su"> Yu-Siang Su</a>, <a href="https://publications.waset.org/abstracts/search?q=Pau-Choo%20Chung"> Pau-Choo Chung</a>, <a href="https://publications.waset.org/abstracts/search?q=Jar-Ferr%20Yang"> Jar-Ferr Yang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Moving object detection (MOD) is an important issue in advanced driver assistance systems (ADAS). There are two important moving objects, pedestrians and scooters in ADAS. In real-world systems, there exist two important challenges for MOD, including the computational complexity and the detection accuracy. The histogram of oriented gradient (HOG) features can easily detect the edge of object without invariance to changes in illumination and shadowing. However, to reduce the execution time for real-time systems, the image size should be down sampled which would lead the outlier influence to increase. For this reason, we propose the histogram of uniformly-oriented gradient (HUG) features to get better accurate description of the contour of human body. In the testing phase, the support vector machine (SVM) with linear kernel function is involved. Experimental results show the correctness and effectiveness of the proposed method. With SVM classifiers, the real testing results show the proposed HUG features achieve better than classification performance than the HOG ones. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection" title="moving object detection">moving object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20of%20oriented%20gradient" title=" histogram of oriented gradient"> histogram of oriented gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20of%20uniformly-oriented%20gradient" title=" histogram of uniformly-oriented gradient"> histogram of uniformly-oriented gradient</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20support%20vector%20machine" title=" linear support vector machine"> linear support vector machine</a> </p> <a href="https://publications.waset.org/abstracts/62854/moving-object-detection-using-histogram-of-uniformly-oriented-gradient" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62854.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">594</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5378</span> Pyramidal Lucas-Kanade Optical Flow Based Moving Object Detection in Dynamic Scenes</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyojin%20Lim">Hyojin Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Cuong%20Nguyen%20Khac"> Cuong Nguyen Khac</a>, <a href="https://publications.waset.org/abstracts/search?q=Yeongyu%20Choi"> Yeongyu Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ho-Youl%20Jung"> Ho-Youl Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a simple moving object detection, which is based on motion vectors obtained from pyramidal Lucas-Kanade optical flow. The proposed method detects moving objects such as pedestrians, the other vehicles and some obstacles at the front-side of the host vehicle, and it can provide the warning to the driver. Motion vectors are obtained by using pyramidal Lucas-Kanade optical flow, and some outliers are eliminated by comparing the amplitude of each vector with the pre-defined threshold value. The background model is obtained by calculating the mean and the variance of the amplitude of recent motion vectors in the rectangular shaped local region called the cell. The model is applied as the reference to classify motion vectors of moving objects and those of background. Motion vectors are clustered to rectangular regions by using the unsupervised clustering K-means algorithm. Labeling method is applied to label groups which is close to each other, using by distance between each center points of rectangular. Through the simulations tested on four kinds of scenarios such as approaching motorbike, vehicle, and pedestrians to host vehicle, we prove that the proposed is simple but efficient for moving object detection in parking lots. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection" title="moving object detection">moving object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20scene" title=" dynamic scene"> dynamic scene</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20flow" title=" optical flow"> optical flow</a>, <a href="https://publications.waset.org/abstracts/search?q=pyramidal%20optical%20flow" title=" pyramidal optical flow"> pyramidal optical flow</a> </p> <a href="https://publications.waset.org/abstracts/50958/pyramidal-lucas-kanade-optical-flow-based-moving-object-detection-in-dynamic-scenes" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50958.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5377</span> User Authentication Using Graphical Password with Sound Signature</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Devi%20Srinivas">Devi Srinivas</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Sindhuja"> K. Sindhuja</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents architecture to improve surveillance applications based on the usage of the service oriented paradigm, with smart phones as user terminals, allowing application dynamic composition and increasing the flexibility of the system. According to the result of moving object detection research on video sequences, the movement of the people is tracked using video surveillance. The moving object is identified using the image subtraction method. The background image is subtracted from the foreground image, from that the moving object is derived. So the Background subtraction algorithm and the threshold value is calculated to find the moving image by using background subtraction algorithm the moving frame is identified. Then, by the threshold value the movement of the frame is identified and tracked. Hence, the movement of the object is identified accurately. This paper deals with low-cost intelligent mobile phone-based wireless video surveillance solution using moving object recognition technology. The proposed solution can be useful in various security systems and environmental surveillance. The fundamental rule of moving object detecting is given in the paper, then, a self-adaptive background representation that can update automatically and timely to adapt to the slow and slight changes of normal surroundings is detailed. While the subtraction of the present captured image and the background reaches a certain threshold, a moving object is measured to be in the current view, and the mobile phone will automatically notify the central control unit or the user through SMS (Short Message System). The main advantage of this system is when an unknown image is captured by the system it will alert the user automatically by sending an SMS to user’s mobile. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=security" title="security">security</a>, <a href="https://publications.waset.org/abstracts/search?q=graphical%20password" title=" graphical password"> graphical password</a>, <a href="https://publications.waset.org/abstracts/search?q=persuasive%20cued%20click%20points" title=" persuasive cued click points"> persuasive cued click points</a> </p> <a href="https://publications.waset.org/abstracts/23794/user-authentication-using-graphical-password-with-sound-signature" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/23794.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">537</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5376</span> Evaluation of Real-Time Background Subtraction Technique for Moving Object Detection Using Fast-Independent Component Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naoum%20Abderrahmane">Naoum Abderrahmane</a>, <a href="https://publications.waset.org/abstracts/search?q=Boumehed%20Meriem"> Boumehed Meriem</a>, <a href="https://publications.waset.org/abstracts/search?q=Alshaqaqi%20Belal"> Alshaqaqi Belal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background subtraction algorithm is a larger used technique for detecting moving objects in video surveillance to extract the foreground objects from a reference background image. There are many challenges to test a good background subtraction algorithm, like changes in illumination, dynamic background such as swinging leaves, rain, snow, and the changes in the background, for example, moving and stopping of vehicles. In this paper, we propose an efficient and accurate background subtraction method for moving object detection in video surveillance. The main idea is to use a developed fast-independent component analysis (ICA) algorithm to separate background, noise, and foreground masks from an image sequence in practical environments. The fast-ICA algorithm is adapted and adjusted with a matrix calculation and searching for an optimum non-quadratic function to be faster and more robust. Moreover, in order to estimate the de-mixing matrix and the denoising de-mixing matrix parameters, we propose to convert all images to YCrCb color space, where the luma component Y (brightness of the color) gives suitable results. The proposed technique has been verified on the publicly available datasets CD net 2012 and CD net 2014, and experimental results show that our algorithm can detect competently and accurately moving objects in challenging conditions compared to other methods in the literature in terms of quantitative and qualitative evaluations with real-time frame rate. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=background%20subtraction" title="background subtraction">background subtraction</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection" title=" moving object detection"> moving object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=fast-ICA" title=" fast-ICA"> fast-ICA</a>, <a href="https://publications.waset.org/abstracts/search?q=de-mixing%20matrix" title=" de-mixing matrix"> de-mixing matrix</a> </p> <a href="https://publications.waset.org/abstracts/156716/evaluation-of-real-time-background-subtraction-technique-for-moving-object-detection-using-fast-independent-component-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">96</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5375</span> Vehicular Speed Detection Camera System Using Video Stream</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=C.%20A.%20Anser%20Pasha">C. A. Anser Pasha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new Vehicular Speed Detection Camera System that is applicable as an alternative to traditional radars with the same accuracy or even better is presented. The real-time measurement and analysis of various traffic parameters such as speed and number of vehicles are increasingly required in traffic control and management. Image processing techniques are now considered as an attractive and flexible method for automatic analysis and data collections in traffic engineering. Various algorithms based on image processing techniques have been applied to detect multiple vehicles and track them. The SDCS processes can be divided into three successive phases; the first phase is Objects detection phase, which uses a hybrid algorithm based on combining an adaptive background subtraction technique with a three-frame differencing algorithm which ratifies the major drawback of using only adaptive background subtraction. The second phase is Objects tracking, which consists of three successive operations - object segmentation, object labeling, and object center extraction. Objects tracking operation takes into consideration the different possible scenarios of the moving object like simple tracking, the object has left the scene, the object has entered the scene, object crossed by another object, and object leaves and another one enters the scene. The third phase is speed calculation phase, which is calculated from the number of frames consumed by the object to pass by the scene. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=radar" title="radar">radar</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=detection" title=" detection"> detection</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=segmentation" title=" segmentation"> segmentation</a> </p> <a href="https://publications.waset.org/abstracts/45316/vehicular-speed-detection-camera-system-using-video-stream" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45316.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">467</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5374</span> Mosaic Augmentation: Insights and Limitations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Olivia%20A.%20Kjorlien">Olivia A. Kjorlien</a>, <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Asghari"> Maryam Asghari</a>, <a href="https://publications.waset.org/abstracts/search?q=Farshid%20Alizadeh-Shabdiz"> Farshid Alizadeh-Shabdiz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The goal of this paper is to investigate the impact of mosaic augmentation on the performance of object detection solutions. To carry out the study, YOLOv4 and YOLOv4-Tiny models have been selected, which are popular, advanced object detection models. These models are also representatives of two classes of complex and simple models. The study also has been carried out on two categories of objects, simple and complex. For this study, YOLOv4 and YOLOv4 Tiny are trained with and without mosaic augmentation for two sets of objects. While mosaic augmentation improves the performance of simple object detection, it deteriorates the performance of complex object detection, specifically having the largest negative impact on the false positive rate in a complex object detection case. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=accuracy" title="accuracy">accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=false%20positives" title=" false positives"> false positives</a>, <a href="https://publications.waset.org/abstracts/search?q=mosaic%20augmentation" title=" mosaic augmentation"> mosaic augmentation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOV4" title=" YOLOV4"> YOLOV4</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLOV4-Tiny" title=" YOLOV4-Tiny"> YOLOV4-Tiny</a> </p> <a href="https://publications.waset.org/abstracts/162634/mosaic-augmentation-insights-and-limitations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162634.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5373</span> Automatic Motion Trajectory Analysis for Dual Human Interaction Using Video Sequences</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yuan-Hsiang%20Chang">Yuan-Hsiang Chang</a>, <a href="https://publications.waset.org/abstracts/search?q=Pin-Chi%20Lin"> Pin-Chi Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Li-Der%20Jeng"> Li-Der Jeng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=motion%20detection" title="motion detection">motion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20tracking" title=" motion tracking"> motion tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=trajectory%20analysis" title=" trajectory analysis"> trajectory analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a> </p> <a href="https://publications.waset.org/abstracts/13650/automatic-motion-trajectory-analysis-for-dual-human-interaction-using-video-sequences" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13650.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">548</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5372</span> Facility Detection from Image Using Mathematical Morphology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=In-Geun%20Lim">In-Geun Lim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sung-Woong%20Ra"> Sung-Woong Ra</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As high resolution satellite images can be used, lots of studies are carried out for exploiting these images in various fields. This paper proposes the method based on mathematical morphology for extracting the ‘horse's hoof shaped object’. This proposed method can make an automatic object detection system to track the meaningful object in a large satellite image rapidly. Mathematical morphology process can apply in binary image, so this method is very simple. Therefore this method can easily extract the ‘horse's hoof shaped object’ from any images which have indistinct edges of the tracking object and have different image qualities depending on filming location, filming time, and filming environment. Using the proposed method by which ‘horse's hoof shaped object’ can be rapidly extracted, the performance of the automatic object detection system can be improved dramatically. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=facility%20detection" title="facility detection">facility detection</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20image" title=" satellite image"> satellite image</a>, <a href="https://publications.waset.org/abstracts/search?q=object" title=" object"> object</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20morphology" title=" mathematical morphology"> mathematical morphology</a> </p> <a href="https://publications.waset.org/abstracts/67611/facility-detection-from-image-using-mathematical-morphology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/67611.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">382</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5371</span> Specified Human Motion Recognition and Unknown Hand-Held Object Tracking</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinsiang%20Shaw">Jinsiang Shaw</a>, <a href="https://publications.waset.org/abstracts/search?q=Pik-Hoe%20Chen"> Pik-Hoe Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to integrate human recognition, motion recognition, and object tracking technologies without requiring a pre-training database model for motion recognition or the unknown object itself. Furthermore, it can simultaneously track multiple users and multiple objects. Unlike other existing human motion recognition methods, our approach employs a rule-based condition method to determine if a user hand is approaching or departing an object. It uses a background subtraction method to separate the human and object from the background, and employs behavior features to effectively interpret human object-grabbing actions. With an object’s histogram characteristics, we are able to isolate and track it using back projection. Hence, a moving object trajectory can be recorded and the object itself can be located. This particular technique can be used in a camera surveillance system in a shopping area to perform real-time intelligent surveillance, thus preventing theft. Experimental results verify the validity of the developed surveillance algorithm with an accuracy of 83% for shoplifting detection. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Automatic%20Tracking" title="Automatic Tracking">Automatic Tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=Back%20Projection" title=" Back Projection"> Back Projection</a>, <a href="https://publications.waset.org/abstracts/search?q=Motion%20Recognition" title=" Motion Recognition"> Motion Recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=Shoplifting" title=" Shoplifting"> Shoplifting</a> </p> <a href="https://publications.waset.org/abstracts/66866/specified-human-motion-recognition-and-unknown-hand-held-object-tracking" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66866.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">333</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5370</span> GPU Based Real-Time Floating Object Detection System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jie%20Yang">Jie Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jian-Min%20Meng"> Jian-Min Meng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A GPU-based floating object detection scheme is presented in this paper which is designed for floating mine detection tasks. This system uses contrast and motion information to eliminate as many false positives as possible while avoiding false negatives. The GPU computation platform is deployed to allow detecting objects in real-time. From the experimental results, it is shown that with certain configuration, the GPU-based scheme can speed up the computation up to one thousand times compared to the CPU-based scheme. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title="object detection">object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=GPU" title=" GPU"> GPU</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20estimation" title=" motion estimation"> motion estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=parallel%20processing" title=" parallel processing"> parallel processing</a> </p> <a href="https://publications.waset.org/abstracts/54425/gpu-based-real-time-floating-object-detection-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54425.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">474</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5369</span> Toward Indoor and Outdoor Surveillance using an Improved Fast Background Subtraction Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=El%20Harraj%20Abdeslam">El Harraj Abdeslam</a>, <a href="https://publications.waset.org/abstracts/search?q=Raissouni%20Naoufal"> Raissouni Naoufal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title="video surveillance">video surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20subtraction" title=" background subtraction"> background subtraction</a>, <a href="https://publications.waset.org/abstracts/search?q=contrast%20limited%20histogram%20equalization" title=" contrast limited histogram equalization"> contrast limited histogram equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=illumination%20invariance" title=" illumination invariance"> illumination invariance</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title=" object tracking"> object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=behavior%20understanding" title=" behavior understanding"> behavior understanding</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20scenes" title=" dynamic scenes"> dynamic scenes</a> </p> <a href="https://publications.waset.org/abstracts/27499/toward-indoor-and-outdoor-surveillance-using-an-improved-fast-background-subtraction-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27499.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5368</span> Object Detection in Digital Images under Non-Standardized Conditions Using Illumination and Shadow Filtering</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Waqqas-ur-Rehman%20Butt">Waqqas-ur-Rehman Butt</a>, <a href="https://publications.waset.org/abstracts/search?q=Martin%20Servin"> Martin Servin</a>, <a href="https://publications.waset.org/abstracts/search?q=Marion%20Pause"> Marion Pause</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=illumination%20equalization" title=" illumination equalization"> illumination equalization</a>, <a href="https://publications.waset.org/abstracts/search?q=shadow%20filtering" title=" shadow filtering"> shadow filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a> </p> <a href="https://publications.waset.org/abstracts/77157/object-detection-in-digital-images-under-non-standardized-conditions-using-illumination-and-shadow-filtering" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77157.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">216</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5367</span> Automatic Detection and Update of Region of Interest in Vehicular Traffic Surveillance Videos</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Naydelis%20Brito%20Su%C3%A1rez">Naydelis Brito Suárez</a>, <a href="https://publications.waset.org/abstracts/search?q=Deni%20Librado%20Torres%20Rom%C3%A1n"> Deni Librado Torres Román</a>, <a href="https://publications.waset.org/abstracts/search?q=Fernando%20Hermosillo%20Reynoso"> Fernando Hermosillo Reynoso</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Automatic detection and generation of a dynamic ROI (Region of Interest) in vehicle traffic surveillance videos based on a static camera in Intelligent Transportation Systems is challenging for computer vision-based systems. The dynamic ROI, being a changing ROI, should capture any other moving object located outside of a static ROI. In this work, the video is represented by a Tensor model composed of a Background and a Foreground Tensor, which contains all moving vehicles or objects. The values of each pixel over a time interval are represented by time series, and some pixel rows were selected. This paper proposes a pixel entropy-based algorithm for automatic detection and generation of a dynamic ROI in traffic videos under the assumption of two types of theoretical pixel entropy behaviors: (1) a pixel located at the road shows a high entropy value due to disturbances in this zone by vehicle traffic, (2) a pixel located outside the road shows a relatively low entropy value. To study the statistical behavior of the selected pixels, detecting the entropy changes and consequently moving objects, Shannon, Tsallis, and Approximate entropies were employed. Although Tsallis entropy achieved very high results in real-time, Approximate entropy showed results slightly better but in greater time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convex%20hull" title="convex hull">convex hull</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20ROI%20detection" title=" dynamic ROI detection"> dynamic ROI detection</a>, <a href="https://publications.waset.org/abstracts/search?q=pixel%20entropy" title=" pixel entropy"> pixel entropy</a>, <a href="https://publications.waset.org/abstracts/search?q=time%20series" title=" time series"> time series</a>, <a href="https://publications.waset.org/abstracts/search?q=moving%20objects" title=" moving objects"> moving objects</a> </p> <a href="https://publications.waset.org/abstracts/174020/automatic-detection-and-update-of-region-of-interest-in-vehicular-traffic-surveillance-videos" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174020.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5366</span> 3D Object Detection for Autonomous Driving: A Comprehensive Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmed%20Soliman%20Nagiub">Ahmed Soliman Nagiub</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahmoud%20Fayez"> Mahmoud Fayez</a>, <a href="https://publications.waset.org/abstracts/search?q=Heba%20Khaled"> Heba Khaled</a>, <a href="https://publications.waset.org/abstracts/search?q=Said%20Ghoniemy"> Said Ghoniemy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title="computer vision">computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20object%20detection" title=" 3D object detection"> 3D object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=autonomous%20vehicles" title=" autonomous vehicles"> autonomous vehicles</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/178070/3d-object-detection-for-autonomous-driving-a-comprehensive-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/178070.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">62</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5365</span> Challenges in Video Based Object Detection in Maritime Scenario Using Computer Vision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dilip%20K.%20Prasad">Dilip K. Prasad</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Krishna%20Prasath"> C. Krishna Prasath</a>, <a href="https://publications.waset.org/abstracts/search?q=Deepu%20Rajan"> Deepu Rajan</a>, <a href="https://publications.waset.org/abstracts/search?q=Lily%20Rachmawati"> Lily Rachmawati</a>, <a href="https://publications.waset.org/abstracts/search?q=Eshan%20Rajabally"> Eshan Rajabally</a>, <a href="https://publications.waset.org/abstracts/search?q=Chai%20Quek"> Chai Quek</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses the technical challenges in maritime image processing and machine vision problems for video streams generated by cameras. Even well documented problems of horizon detection and registration of frames in a video are very challenging in maritime scenarios. More advanced problems of background subtraction and object detection in video streams are very challenging. Challenges arising from the dynamic nature of the background, unavailability of static cues, presence of small objects at distant backgrounds, illumination effects, all contribute to the challenges as discussed here. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20maritime%20vehicle" title="autonomous maritime vehicle">autonomous maritime vehicle</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=situation%20awareness" title=" situation awareness"> situation awareness</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a> </p> <a href="https://publications.waset.org/abstracts/54887/challenges-in-video-based-object-detection-in-maritime-scenario-using-computer-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54887.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">458</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5364</span> Roof Material Detection Based on Object-Based Approach Using WorldView-2 Satellite Imagery</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ebrahim%20Taherzadeh">Ebrahim Taherzadeh</a>, <a href="https://publications.waset.org/abstracts/search?q=Helmi%20Z.%20M.%20Shafri"> Helmi Z. M. Shafri</a>, <a href="https://publications.waset.org/abstracts/search?q=Kaveh%20Shahi"> Kaveh Shahi </a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important tasks in urban area remote sensing is detection of impervious surface (IS), such as building roof and roads. However, detection of IS in heterogeneous areas still remains as one of the most challenging works. In this study, detection of concrete roof using an object-oriented approach was proposed. A new rule-based classification was developed to detect concrete roof tile. The proposed rule-based classification was applied to WorldView-2 image. Results showed that the proposed rule has good potential to predict concrete roof material from WorldView-2 images with 85% accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object-based" title="object-based">object-based</a>, <a href="https://publications.waset.org/abstracts/search?q=roof%20material" title=" roof material"> roof material</a>, <a href="https://publications.waset.org/abstracts/search?q=concrete%20tile" title=" concrete tile"> concrete tile</a>, <a href="https://publications.waset.org/abstracts/search?q=WorldView-2" title=" WorldView-2"> WorldView-2</a> </p> <a href="https://publications.waset.org/abstracts/13685/roof-material-detection-based-on-object-based-approach-using-worldview-2-satellite-imagery" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13685.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">424</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5363</span> The Study on How Social Cues in a Scene Modulate Basic Object Recognition Proces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shih-Yu%20Lo">Shih-Yu Lo</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=gender%20stereotype" title="gender stereotype">gender stereotype</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20recognition" title=" object recognition"> object recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20detection%20theory" title=" signal detection theory"> signal detection theory</a>, <a href="https://publications.waset.org/abstracts/search?q=weapon" title=" weapon"> weapon</a> </p> <a href="https://publications.waset.org/abstracts/92535/the-study-on-how-social-cues-in-a-scene-modulate-basic-object-recognition-proces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/92535.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">209</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5362</span> On Enabling Miner Self-Rescue with In-Mine Robots using Real-Time Object Detection with Thermal Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cyrus%20Addy">Cyrus Addy</a>, <a href="https://publications.waset.org/abstracts/search?q=Venkata%20Sriram%20Siddhardh%20Nadendla"> Venkata Sriram Siddhardh Nadendla</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwame%20Awuah-Offei"> Kwame Awuah-Offei</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Surface robots in modern underground mine rescue operations suffer from several limitations in enabling a prompt self-rescue. Therefore, the possibility of designing and deploying in-mine robots to expedite miner self-rescue can have a transformative impact on miner safety. These in-mine robots for miner self-rescue can be envisioned to carry out diverse tasks such as object detection, autonomous navigation, and payload delivery. Specifically, this paper investigates the challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time. A total of 125 thermal images were collected in the Missouri S&T Experimental Mine with the help of student volunteers using the FLIR TG 297 infrared camera, which were pre-processed into training and validation datasets with 100 and 25 images, respectively. Three state-of-the-art, pre-trained real-time object detection models, namely YOLOv5, YOLO-FIRI, and YOLOv8, were considered and re-trained using transfer learning techniques on the training dataset. On the validation dataset, the re-trained YOLOv8 outperforms the re-trained versions of both YOLOv5, and YOLO-FIRI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=miner%20self-rescue" title="miner self-rescue">miner self-rescue</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=underground%20mine" title=" underground mine"> underground mine</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLO" title=" YOLO"> YOLO</a> </p> <a href="https://publications.waset.org/abstracts/174124/on-enabling-miner-self-rescue-with-in-mine-robots-using-real-time-object-detection-with-thermal-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/174124.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">83</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5361</span> A Comprehensive Study of Camouflaged Object Detection Using Deep Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khalak%20Bin%20Khair">Khalak Bin Khair</a>, <a href="https://publications.waset.org/abstracts/search?q=Saqib%20Jahir"> Saqib Jahir</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Ibrahim"> Mohammed Ibrahim</a>, <a href="https://publications.waset.org/abstracts/search?q=Fahad%20Bin"> Fahad Bin</a>, <a href="https://publications.waset.org/abstracts/search?q=Debajyoti%20Karmaker"> Debajyoti Karmaker</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title="deep learning">deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning" title=" transfer learning"> transfer learning</a>, <a href="https://publications.waset.org/abstracts/search?q=TensorFlow" title=" TensorFlow"> TensorFlow</a>, <a href="https://publications.waset.org/abstracts/search?q=camouflage" title=" camouflage"> camouflage</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title=" object detection"> object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=architecture" title=" architecture"> architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=accuracy" title=" accuracy"> accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=model" title=" model"> model</a>, <a href="https://publications.waset.org/abstracts/search?q=VGG16" title=" VGG16"> VGG16</a> </p> <a href="https://publications.waset.org/abstracts/152633/a-comprehensive-study-of-camouflaged-object-detection-using-deep-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152633.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">149</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5360</span> Object Detection Based on Plane Segmentation and Features Matching for a Service Robot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ant%C3%B3nio%20J.%20R.%20Neves">António J. R. Neves</a>, <a href="https://publications.waset.org/abstracts/search?q=Rui%20Garcia"> Rui Garcia</a>, <a href="https://publications.waset.org/abstracts/search?q=Paulo%20Dias"> Paulo Dias</a>, <a href="https://publications.waset.org/abstracts/search?q=Alina%20Trifan"> Alina Trifan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=object%20detection" title="object detection">object detection</a>, <a href="https://publications.waset.org/abstracts/search?q=feature" title=" feature"> feature</a>, <a href="https://publications.waset.org/abstracts/search?q=descriptors" title=" descriptors"> descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=SIFT" title=" SIFT"> SIFT</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF" title=" SURF"> SURF</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20images" title=" depth images"> depth images</a>, <a href="https://publications.waset.org/abstracts/search?q=service%20robots" title=" service robots"> service robots</a> </p> <a href="https://publications.waset.org/abstracts/39840/object-detection-based-on-plane-segmentation-and-features-matching-for-a-service-robot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/39840.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">546</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5359</span> Video Object Segmentation for Automatic Image Annotation of Ethernet Connectors with Environment Mapping and 3D Projection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marrone%20Silverio%20Melo%20Dantas%20Pedro%20Henrique%20Dreyer">Marrone Silverio Melo Dantas Pedro Henrique Dreyer</a>, <a href="https://publications.waset.org/abstracts/search?q=Gabriel%20Fonseca%20Reis%20de%20Souza"> Gabriel Fonseca Reis de Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Bezerra"> Daniel Bezerra</a>, <a href="https://publications.waset.org/abstracts/search?q=Ricardo%20Souza"> Ricardo Souza</a>, <a href="https://publications.waset.org/abstracts/search?q=Silvia%20Lins"> Silvia Lins</a>, <a href="https://publications.waset.org/abstracts/search?q=Judith%20Kelner"> Judith Kelner</a>, <a href="https://publications.waset.org/abstracts/search?q=Djamel%20Fawzi%20Hadj%20Sadok"> Djamel Fawzi Hadj Sadok</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The creation of a dataset is time-consuming and often discourages researchers from pursuing their goals. To overcome this problem, we present and discuss two solutions adopted for the automation of this process. Both optimize valuable user time and resources and support video object segmentation with object tracking and 3D projection. In our scenario, we acquire images from a moving robotic arm and, for each approach, generate distinct annotated datasets. We evaluated the precision of the annotations by comparing these with a manually annotated dataset, as well as the efficiency in the context of detection and classification problems. For detection support, we used YOLO and obtained for the projection dataset an F1-Score, accuracy, and mAP values of 0.846, 0.924, and 0.875, respectively. Concerning the tracking dataset, we achieved an F1-Score of 0.861, an accuracy of 0.932, whereas mAP reached 0.894. In order to evaluate the quality of the annotated images used for classification problems, we employed deep learning architectures. We adopted metrics accuracy and F1-Score, for VGG, DenseNet, MobileNet, Inception, and ResNet. The VGG architecture outperformed the others for both projection and tracking datasets. It reached an accuracy and F1-score of 0.997 and 0.993, respectively. Similarly, for the tracking dataset, it achieved an accuracy of 0.991 and an F1-Score of 0.981. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=RJ45" title="RJ45">RJ45</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20annotation" title=" automatic annotation"> automatic annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20tracking" title=" object tracking"> object tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20projection" title=" 3D projection"> 3D projection</a> </p> <a href="https://publications.waset.org/abstracts/130540/video-object-segmentation-for-automatic-image-annotation-of-ethernet-connectors-with-environment-mapping-and-3d-projection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130540.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">167</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5358</span> Searching k-Nearest Neighbors to be Appropriate under Gaming Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae%20Moon%20Lee">Jae Moon Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In general, algorithms to find continuous k-nearest neighbors have been researched on the location based services, monitoring periodically the moving objects such as vehicles and mobile phone. Those researches assume the environment that the number of query points is much less than that of moving objects and the query points are not moved but fixed. In gaming environments, this problem is when computing the next movement considering the neighbors such as flocking, crowd and robot simulations. In this case, every moving object becomes a query point so that the number of query point is same to that of moving objects and the query points are also moving. In this paper, we analyze the performance of the existing algorithms focused on location based services how they operate under gaming environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=flocking%20behavior" title="flocking behavior">flocking behavior</a>, <a href="https://publications.waset.org/abstracts/search?q=heterogeneous%20agents" title=" heterogeneous agents"> heterogeneous agents</a>, <a href="https://publications.waset.org/abstracts/search?q=similarity" title=" similarity"> similarity</a>, <a href="https://publications.waset.org/abstracts/search?q=simulation" title=" simulation"> simulation</a> </p> <a href="https://publications.waset.org/abstracts/8228/searching-k-nearest-neighbors-to-be-appropriate-under-gaming-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/8228.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5357</span> Enhanced Acquisition Time of a Quantum Holography Scheme within a Nonlinear Interferometer</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergio%20Tovar-P%C3%A9rez">Sergio Tovar-Pérez</a>, <a href="https://publications.waset.org/abstracts/search?q=Sebastian%20T%C3%B6pfer"> Sebastian Töpfer</a>, <a href="https://publications.waset.org/abstracts/search?q=Markus%20Gr%C3%A4fe"> Markus Gräfe</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The work proposes a technique that decreases the detection acquisition time of quantum holography schemes down to one-third; this allows the possibility to image moving objects. Since its invention, quantum holography with undetected photon schemes has gained interest in the scientific community. This is mainly due to its ability to tailor the detected wavelengths according to the needs of the scheme implementation. Yet this wavelength flexibility grants the scheme a wide range of possible applications; an important matter was yet to be addressed. Since the scheme uses digital phase-shifting techniques to retrieve the information of the object out of the interference pattern, it is necessary to acquire a set of at least four images of the interference pattern along with well-defined phase steps to recover the full object information. Hence, the imaging method requires larger acquisition times to produce well-resolved images. As a consequence, the measurement of moving objects remains out of the reach of the imaging scheme. This work presents the use and implementation of a spatial light modulator along with a digital holographic technique called quasi-parallel phase-shifting. This technique uses the spatial light modulator to build a structured phase image consisting of a chessboard pattern containing the different phase steps for digitally calculating the object information. Depending on the reduction in the number of needed frames, the acquisition time reduces by a significant factor. This technique opens the door to the implementation of the scheme for moving objects. In particular, the application of this scheme in imaging alive specimens comes one step closer. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quasi-parallel%20phase%20shifting" title="quasi-parallel phase shifting">quasi-parallel phase shifting</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20imaging" title=" quantum imaging"> quantum imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20holography" title=" quantum holography"> quantum holography</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20metrology" title=" quantum metrology"> quantum metrology</a> </p> <a href="https://publications.waset.org/abstracts/156520/enhanced-acquisition-time-of-a-quantum-holography-scheme-within-a-nonlinear-interferometer" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/156520.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">114</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5356</span> Motion-Based Detection and Tracking of Multiple Pedestrians</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Harras">A. Harras</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tsuji"> A. Tsuji</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20Terada"> K. Terada</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20detection" title="automatic detection">automatic detection</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrians" title=" pedestrians"> pedestrians</a>, <a href="https://publications.waset.org/abstracts/search?q=counting" title=" counting"> counting</a> </p> <a href="https://publications.waset.org/abstracts/82912/motion-based-detection-and-tracking-of-multiple-pedestrians" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82912.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">257</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5355</span> Vehicle Detection and Tracking Using Deep Learning Techniques in Surveillance Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abe%20D.%20Desta">Abe D. Desta</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20intelligence" title="artificial intelligence">artificial intelligence</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=fast-regional%20convolutional%20neural%20networks" title=" fast-regional convolutional neural networks"> fast-regional convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20tracking" title=" vehicle tracking"> vehicle tracking</a> </p> <a href="https://publications.waset.org/abstracts/164803/vehicle-detection-and-tracking-using-deep-learning-techniques-in-surveillance-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164803.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">126</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5354</span> Clustering Color Space, Time Interest Points for Moving Objects</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Insaf%20Bellamine">Insaf Bellamine</a>, <a href="https://publications.waset.org/abstracts/search?q=Hamid%20Tairi"> Hamid Tairi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Color%20Space-Time%20Interest%20Points%20%28CSTIP%29" title="Color Space-Time Interest Points (CSTIP)">Color Space-Time Interest Points (CSTIP)</a>, <a href="https://publications.waset.org/abstracts/search?q=Color%20Structure-Texture%20Image%20Decomposition" title=" Color Structure-Texture Image Decomposition"> Color Structure-Texture Image Decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=Motion%20Detection" title=" Motion Detection"> Motion Detection</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a> </p> <a href="https://publications.waset.org/abstracts/21989/clustering-color-space-time-interest-points-for-moving-objects" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21989.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">378</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=moving%20object%20detection&page=4">4</a></li> <li class="page-item"><a class="page-link" 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