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detection with multimodal inputs</title> <meta name="description" content="Search results for: object detection with multimodal inputs"> <meta name="keywords" content="object detection with multimodal inputs"> <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 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method="get" 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="object detection with multimodal inputs"> <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> 5279</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: object detection with multimodal inputs</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5279</span> Multichannel Object Detection with Event Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Rafael%20Iliasov">Rafael Iliasov</a>, <a href="https://publications.waset.org/abstracts/search?q=Alessandro%20Golkar"> Alessandro Golkar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion method (EFM) to enhance object detection in event-based vision systems. Each channel uses a different accumulation buffer to collect events from the event camera. We implement YOLOv7 for object detection, followed by a fusion algorithm. Our multichannel approach outperforms single-channel-based object detection by 0.7% in mean Average Precision (mAP) for detection overlapping ground truth with IOU = 0.5. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=event%20camera" title="event camera">event camera</a>, <a href="https://publications.waset.org/abstracts/search?q=object%20detection%20with%20multimodal%20inputs" title=" object detection with multimodal inputs"> object detection with multimodal inputs</a>, <a href="https://publications.waset.org/abstracts/search?q=multichannel%20fusion" title=" multichannel fusion"> multichannel fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a> </p> <a href="https://publications.waset.org/abstracts/190247/multichannel-object-detection-with-event-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/190247.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">27</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">5278</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">5277</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">381</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">5276</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">5275</span> Multimodal Characterization of Emotion within Multimedia Space</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dayo%20Samuel%20Banjo">Dayo Samuel Banjo</a>, <a href="https://publications.waset.org/abstracts/search?q=Connice%20Trimmingham"> Connice Trimmingham</a>, <a href="https://publications.waset.org/abstracts/search?q=Niloofar%20Yousefi"> Niloofar Yousefi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nitin%20Agarwal"> Nitin Agarwal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Technological advancement and its omnipresent connection have pushed humans past the boundaries and limitations of a computer screen, physical state, or geographical location. It has provided a depth of avenues that facilitate human-computer interaction that was once inconceivable such as audio and body language detection. Given the complex modularities of emotions, it becomes vital to study human-computer interaction, as it is the commencement of a thorough understanding of the emotional state of users and, in the context of social networks, the producers of multimodal information. This study first acknowledges the accuracy of classification found within multimodal emotion detection systems compared to unimodal solutions. Second, it explores the characterization of multimedia content produced based on their emotions and the coherence of emotion in different modalities by utilizing deep learning models to classify emotion across different modalities. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=affective%20computing" title="affective computing">affective computing</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=emotion%20recognition" title=" emotion recognition"> emotion recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal" title=" multimodal"> multimodal</a> </p> <a href="https://publications.waset.org/abstracts/157830/multimodal-characterization-of-emotion-within-multimedia-space" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/157830.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">156</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">5274</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">5273</span> TMIF: Transformer-Based Multi-Modal Interactive Fusion for Rumor Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jiandong%20Lv">Jiandong Lv</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingang%20Wang"> Xingang Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Cuiling%20Shao"> Cuiling Shao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The rapid development of social media platforms has made it one of the important news sources. While it provides people with convenient real-time communication channels, fake news and rumors are also spread rapidly through social media platforms, misleading the public and even causing bad social impact in view of the slow speed and poor consistency of artificial rumor detection. We propose an end-to-end rumor detection model-TIMF, which captures the dependencies between multimodal data based on the interactive attention mechanism, uses a transformer for cross-modal feature sequence mapping and combines hybrid fusion strategies to obtain decision results. This paper verifies two multi-modal rumor detection datasets and proves the superior performance and early detection performance of the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hybrid%20fusion" title="hybrid fusion">hybrid fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20fusion" title=" multimodal fusion"> multimodal fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=rumor%20detection" title=" rumor detection"> rumor detection</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20media" title=" social media"> social media</a>, <a href="https://publications.waset.org/abstracts/search?q=transformer" title=" transformer"> transformer</a> </p> <a href="https://publications.waset.org/abstracts/141806/tmif-transformer-based-multi-modal-interactive-fusion-for-rumor-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/141806.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">246</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">5272</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">5271</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">407</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">5270</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">5269</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">5268</span> Modeling of Building a Conceptual Scheme for Multimodal Freight Transportation Information System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gia%20Surguladze">Gia Surguladze</a>, <a href="https://publications.waset.org/abstracts/search?q=Nino%20Topuria"> Nino Topuria</a>, <a href="https://publications.waset.org/abstracts/search?q=Lily%20Petriashvili"> Lily Petriashvili</a>, <a href="https://publications.waset.org/abstracts/search?q=Giorgi%20Surguladze"> Giorgi Surguladze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modeling of building processes of a multimodal freight transportation support information system is discussed based on modern CASE technologies. Functional efficiencies of ports in the eastern part of the Black Sea are analyzed taking into account their ecological, seasonal, resource usage parameters. By resources, we mean capacities of berths, cranes, automotive transport, as well as work crews and neighbouring airports. For the purpose of designing database of computer support system for Managerial (Logistics) function, using Object-Role Modeling (ORM) tool (NORMA – Natural ORM Architecture) is proposed, after which Entity Relationship Model (ERM) is generated in automated process. The software is developed based on Process-Oriented and Service-Oriented architecture, in Visual Studio.NET environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=seaport%20resources" title="seaport resources">seaport resources</a>, <a href="https://publications.waset.org/abstracts/search?q=business-processes" title=" business-processes"> business-processes</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20transportation" title=" multimodal transportation"> multimodal transportation</a>, <a href="https://publications.waset.org/abstracts/search?q=CASE%20technology" title=" CASE technology"> CASE technology</a>, <a href="https://publications.waset.org/abstracts/search?q=object-role%20model" title=" object-role model"> object-role model</a>, <a href="https://publications.waset.org/abstracts/search?q=entity%20relationship%20model" title=" entity relationship model"> entity relationship model</a>, <a href="https://publications.waset.org/abstracts/search?q=SOA" title=" SOA"> SOA</a> </p> <a href="https://publications.waset.org/abstracts/32046/modeling-of-building-a-conceptual-scheme-for-multimodal-freight-transportation-information-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32046.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">430</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">5267</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">457</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">5266</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">5265</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">5264</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">5263</span> Evaluating Performance of an Anomaly Detection Module with Artificial Neural Network Implementation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Edward%20Guill%C3%A9n">Edward Guillén</a>, <a href="https://publications.waset.org/abstracts/search?q=Jhordany%20Rodriguez"> Jhordany Rodriguez</a>, <a href="https://publications.waset.org/abstracts/search?q=Rafael%20P%C3%A1ez"> Rafael Páez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=network%20intrusion%20detection" title="network intrusion detection">network intrusion detection</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20network" title=" artificial neural network"> artificial neural network</a>, <a href="https://publications.waset.org/abstracts/search?q=anomaly%20detection%20module" title="anomaly detection module">anomaly detection module</a> </p> <a href="https://publications.waset.org/abstracts/2047/evaluating-performance-of-an-anomaly-detection-module-with-artificial-neural-network-implementation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2047.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">5262</span> Identity Verification Based on Multimodal Machine Learning on Red Green Blue (RGB) Red Green Blue-Depth (RGB-D) Voice Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=LuoJiaoyang">LuoJiaoyang</a>, <a href="https://publications.waset.org/abstracts/search?q=Yu%20Hongyang"> Yu Hongyang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we experimented with a new approach to multimodal identification using RGB, RGB-D and voice data. The multimodal combination of RGB and voice data has been applied in tasks such as emotion recognition and has shown good results and stability, and it is also the same in identity recognition tasks. We believe that the data of different modalities can enhance the effect of the model through mutual reinforcement. We try to increase the three modalities on the basis of the dual modalities and try to improve the effectiveness of the network by increasing the number of modalities. We also implemented the single-modal identification system separately, tested the data of these different modalities under clean and noisy conditions, and compared the performance with the multimodal model. In the process of designing the multimodal model, we tried a variety of different fusion strategies and finally chose the fusion method with the best performance. The experimental results show that the performance of the multimodal system is better than that of the single modality, especially in dealing with noise, and the multimodal system can achieve an average improvement of 5%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimodal" title="multimodal">multimodal</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20modalities" title=" three modalities"> three modalities</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB-D" title=" RGB-D"> RGB-D</a>, <a href="https://publications.waset.org/abstracts/search?q=identity%20verification" title=" identity verification"> identity verification</a> </p> <a href="https://publications.waset.org/abstracts/163265/identity-verification-based-on-multimodal-machine-learning-on-red-green-blue-rgb-red-green-blue-depth-rgb-d-voice-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/163265.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">70</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">5261</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">81</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">5260</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">5259</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">545</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">5258</span> A Comparative Study on Multimodal Metaphors in Public Service Advertising of China and Germany</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xing%20Lyu">Xing Lyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Multimodal metaphor promotes the further development and refinement of multimodal discourse study. Cultural aspects matter a lot not only in creating but also in comprehending multimodal metaphor. By analyzing the target domain and the source domain in 10 public service advertisements of China and Germany about environmental protection, this paper compares the source when the target is alike in each multimodal metaphor in order to seek similarities and differences across cultures. The findings are as follows: first, the multimodal metaphors center around three major topics: the earth crisis, consequences of environmental damage, and appeal for environmental protection; second, the multimodal metaphors mainly grounded in three universal conceptual metaphors which focused on high level is up; earth is mother and all lives are precious. However, there are five Chinese culture-specific multimodal metaphors which are not discovered in Germany ads: east is high leve; a purposeful life is a journey; a nation is a person; good is clean, and water is mother. Since metaphors are excellent instruments on studying ideology, this study can be helpful on intercultural/cross-cultural communication. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimodal%20metaphor" title="multimodal metaphor">multimodal metaphor</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20aspects" title=" cultural aspects"> cultural aspects</a>, <a href="https://publications.waset.org/abstracts/search?q=public%20service%20advertising" title=" public service advertising"> public service advertising</a>, <a href="https://publications.waset.org/abstracts/search?q=cross-cultural%20communication" title=" cross-cultural communication"> cross-cultural communication</a> </p> <a href="https://publications.waset.org/abstracts/112889/a-comparative-study-on-multimodal-metaphors-in-public-service-advertising-of-china-and-germany" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112889.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">173</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">5257</span> The Optimization of Decision Rules in Multimodal Decision-Level Fusion Scheme</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andrey%20V.%20Timofeev">Andrey V. Timofeev</a>, <a href="https://publications.waset.org/abstracts/search?q=Dmitry%20V.%20Egorov"> Dmitry V. Egorov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=classification%20accuracy" title="classification accuracy">classification accuracy</a>, <a href="https://publications.waset.org/abstracts/search?q=fusion%20solution" title=" fusion solution"> fusion solution</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20error%20rate" title=" total error rate"> total error rate</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20fusion%20classifier" title=" multimodal fusion classifier"> multimodal fusion classifier</a> </p> <a href="https://publications.waset.org/abstracts/26088/the-optimization-of-decision-rules-in-multimodal-decision-level-fusion-scheme" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26088.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">466</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">5256</span> Improvement of Brain Tumors Detection Using Markers and Boundaries Transform </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yousif%20Mohamed%20Y.%20Abdallah">Yousif Mohamed Y. Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Mommen%20A.%20Alkhir"> Mommen A. Alkhir</a>, <a href="https://publications.waset.org/abstracts/search?q=Amel%20S.%20Algaddal"> Amel S. Algaddal</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This was experimental study conducted to study segmentation of brain in MRI images using edge detection and morphology filters. For brain MRI images each film scanned using digitizer scanner then treated by using image processing program (MatLab), where the segmentation was studied. The scanned image was saved in a TIFF file format to preserve the quality of the image. Brain tissue can be easily detected in MRI image if the object has sufficient contrast from the background. We use edge detection and basic morphology tools to detect a brain. The segmentation of MRI images steps using detection and morphology filters were image reading, detection entire brain, dilation of the image, filling interior gaps inside the image, removal connected objects on borders and smoothen the object (brain). The results of this study were that it showed an alternate method for displaying the segmented object would be to place an outline around the segmented brain. Those filters approaches can help in removal of unwanted background information and increase diagnostic information of Brain MRI. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=improvement" title="improvement">improvement</a>, <a href="https://publications.waset.org/abstracts/search?q=brain" title=" brain"> brain</a>, <a href="https://publications.waset.org/abstracts/search?q=matlab" title=" matlab"> matlab</a>, <a href="https://publications.waset.org/abstracts/search?q=markers" title=" markers"> markers</a>, <a href="https://publications.waset.org/abstracts/search?q=boundaries" title=" boundaries"> boundaries</a> </p> <a href="https://publications.waset.org/abstracts/31036/improvement-of-brain-tumors-detection-using-markers-and-boundaries-transform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31036.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">516</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">5255</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">5254</span> Low-Cost Parking Lot Mapping and Localization for Home Zone Parking Pilot</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hongbo%20Zhang">Hongbo Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xinlu%20Tang"> Xinlu Tang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiangwei%20Li"> Jiangwei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Chi%20Yan"> Chi Yan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Home zone parking pilot (HPP) is a fast-growing segment in low-speed autonomous driving applications. It requires the car automatically cruise around a parking lot and park itself in a range of up to 100 meters inside a recurrent home/office parking lot, which requires precise parking lot mapping and localization solution. Although Lidar is ideal for SLAM, the car OEMs favor a low-cost fish-eye camera based visual SLAM approach. Recent approaches have employed segmentation models to extract semantic features and improve mapping accuracy, but these AI models are memory unfriendly and computationally expensive, making deploying on embedded ADAS systems difficult. To address this issue, we proposed a new method that utilizes object detection models to extract robust and accurate parking lot features. The proposed method could reduce computational costs while maintaining high accuracy. Once combined with vehicles’ wheel-pulse information, the system could construct maps and locate the vehicle in real-time. This article will discuss in detail (1) the fish-eye based Around View Monitoring (AVM) with transparent chassis images as the inputs, (2) an Object Detection (OD) based feature point extraction algorithm to generate point cloud, (3) a low computational parking lot mapping algorithm and (4) the real-time localization algorithm. At last, we will demonstrate the experiment results with an embedded ADAS system installed on a real car in the underground parking lot. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ADAS" title="ADAS">ADAS</a>, <a href="https://publications.waset.org/abstracts/search?q=home%20zone%20parking%20pilot" title=" home zone parking pilot"> home zone parking pilot</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=visual%20SLAM" title=" visual SLAM"> visual SLAM</a> </p> <a href="https://publications.waset.org/abstracts/162272/low-cost-parking-lot-mapping-and-localization-for-home-zone-parking-pilot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/162272.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">67</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">5253</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">5252</span> RV-YOLOX: Object Detection on Inland Waterways Based on Optimized YOLOX Through Fusion of Vision and 3+1D Millimeter Wave Radar</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zixian%20Zhang">Zixian Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Shanliang%20Yao"> Shanliang Yao</a>, <a href="https://publications.waset.org/abstracts/search?q=Zile%20Huang"> Zile Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhaodong%20Wu"> Zhaodong Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaohui%20Zhu"> Xiaohui Zhu</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong%20Yue"> Yong Yue</a>, <a href="https://publications.waset.org/abstracts/search?q=Jieming%20Ma"> Jieming Ma</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Unmanned Surface Vehicles (USVs) are valuable due to their ability to perform dangerous and time-consuming tasks on the water. Object detection tasks are significant in these applications. However, inherent challenges, such as the complex distribution of obstacles, reflections from shore structures, water surface fog, etc., hinder the performance of object detection of USVs. To address these problems, this paper provides a fusion method for USVs to effectively detect objects in the inland surface environment, utilizing vision sensors and 3+1D Millimeter-wave radar. MMW radar is complementary to vision sensors, providing robust environmental information. The radar 3D point cloud is transferred to 2D radar pseudo image to unify radar and vision information format by utilizing the point transformer. We propose a multi-source object detection network (RV-YOLOX )based on radar-vision fusion for inland waterways environment. The performance is evaluated on our self-recording waterways dataset. Compared with the YOLOX network, our fusion network significantly improves detection accuracy, especially for objects with bad light conditions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=inland%20waterways" title="inland waterways">inland waterways</a>, <a href="https://publications.waset.org/abstracts/search?q=YOLO" title=" YOLO"> YOLO</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20fusion" title=" sensor fusion"> sensor fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=self-attention" title=" self-attention"> self-attention</a> </p> <a href="https://publications.waset.org/abstracts/164399/rv-yolox-object-detection-on-inland-waterways-based-on-optimized-yolox-through-fusion-of-vision-and-31d-millimeter-wave-radar" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164399.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">121</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">5251</span> Adaptive Online Object Tracking via Positive and Negative Models Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shaomei%20Li">Shaomei Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Yawen%20Wang"> Yawen Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Chao%20Gao"> Chao Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences. <p class="card-text"><strong>Keywords:</strong> <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=tracking%20drift" title=" tracking drift"> tracking drift</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20least%20squares%20analysis" title=" partial least squares analysis"> partial least squares analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20and%20negative%20models%20matching" title=" positive and negative models matching"> positive and negative models matching</a> </p> <a href="https://publications.waset.org/abstracts/19382/adaptive-online-object-tracking-via-positive-and-negative-models-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19382.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">529</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">5250</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> <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=object%20detection%20with%20multimodal%20inputs&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=object%20detection%20with%20multimodal%20inputs&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=object%20detection%20with%20multimodal%20inputs&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=object%20detection%20with%20multimodal%20inputs&page=5">5</a></li> <li class="page-item"><a class="page-link" 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