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network</title> <meta name="description" content="Search results for: camera network"> <meta name="keywords" content="camera network"> <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 class="d-block d-lg-none navbar-toggler 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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="camera network"> <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> 5263</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: camera network</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">5263</span> Person Re-Identification using Siamese Convolutional Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sello%20Mokwena">Sello Mokwena</a>, <a href="https://publications.waset.org/abstracts/search?q=Monyepao%20Thabang"> Monyepao Thabang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this study, we propose a comprehensive approach to address the challenges in person re-identification models. By combining a centroid tracking algorithm with a Siamese convolutional neural network model, our method excels in detecting, tracking, and capturing robust person features across non-overlapping camera views. The algorithm efficiently identifies individuals in the camera network, while the neural network extracts fine-grained global features for precise cross-image comparisons. The approach's effectiveness is further accentuated by leveraging the camera network topology for guidance. Our empirical analysis on benchmark datasets highlights its competitive performance, particularly evident when background subtraction techniques are selectively applied, underscoring its potential in advancing person re-identification techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20network" title="camera network">camera network</a>, <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20network%20topology" title=" convolutional neural network topology"> convolutional neural network topology</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20tracking" title=" person tracking"> person tracking</a>, <a href="https://publications.waset.org/abstracts/search?q=person%20re-identification" title=" person re-identification"> person re-identification</a>, <a href="https://publications.waset.org/abstracts/search?q=siamese" title=" siamese"> siamese</a> </p> <a href="https://publications.waset.org/abstracts/171989/person-re-identification-using-siamese-convolutional-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171989.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">73</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> An Automated Procedure for Estimating the Glomerular Filtration Rate and Determining the Normality or Abnormality of the Kidney Stages Using an Artificial Neural Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hossain%20A.">Hossain A.</a>, <a href="https://publications.waset.org/abstracts/search?q=Chowdhury%20S.%20I."> Chowdhury S. I.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: The use of a gamma camera is a standard procedure in nuclear medicine facilities or hospitals to diagnose chronic kidney disease (CKD), but the gamma camera does not precisely stage the disease. The authors sought to determine whether they could use an artificial neural network to determine whether CKD was in normal or abnormal stages based on GFR values (ANN). Method: The 250 kidney patients (Training 188, Testing 62) who underwent an ultrasonography test to diagnose a renal test in our nuclear medical center were scanned using a gamma camera. Before the scanning procedure, the patients received an injection of ⁹⁹ᵐTc-DTPA. The gamma camera computes the pre- and post-syringe radioactive counts after the injection has been pushed into the patient's vein. The artificial neural network uses the softmax function with cross-entropy loss to determine whether CKD is normal or abnormal based on the GFR value in the output layer. Results: The proposed ANN model had a 99.20 % accuracy according to K-fold cross-validation. The sensitivity and specificity were 99.10 and 99.20 %, respectively. AUC was 0.994. Conclusion: The proposed model can distinguish between normal and abnormal stages of CKD by using an artificial neural network. The gamma camera could be upgraded to diagnose normal or abnormal stages of CKD with an appropriate GFR value following the clinical application of the proposed model. <p class="card-text"><strong>Keywords:</strong> <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=glomerular%20filtration%20rate" title=" glomerular filtration rate"> glomerular filtration rate</a>, <a href="https://publications.waset.org/abstracts/search?q=stages%20of%20the%20kidney" title=" stages of the kidney"> stages of the kidney</a>, <a href="https://publications.waset.org/abstracts/search?q=gamma%20camera" title=" gamma camera"> gamma camera</a> </p> <a href="https://publications.waset.org/abstracts/153994/an-automated-procedure-for-estimating-the-glomerular-filtration-rate-and-determining-the-normality-or-abnormality-of-the-kidney-stages-using-an-artificial-neural-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/153994.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">103</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> Video Sharing System Based On Wi-fi Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qidi%20Lin">Qidi Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Jinbin%20Huang"> Jinbin Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Weile%20Liang"> Weile Liang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper introduces a video sharing platform based on WiFi, which consists of camera, mobile phone and PC server. This platform can receive wireless signal from the camera and show the live video on the mobile phone captured by camera. In addition that, it is able to send commands to camera and control the camera’s holder to rotate. The platform can be applied to interactive teaching and dangerous area’s monitoring and so on. Testing results show that the platform can share the live video of mobile phone. Furthermore, if the system’s PC sever and the camera and many mobile phones are connected together, it can transfer photos concurrently. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wifi%20Camera" title="Wifi Camera">Wifi Camera</a>, <a href="https://publications.waset.org/abstracts/search?q=socket%20mobile" title=" socket mobile"> socket mobile</a>, <a href="https://publications.waset.org/abstracts/search?q=platform%20video%20monitoring" title=" platform video monitoring"> platform video monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=remote%20control" title=" remote control"> remote control</a> </p> <a href="https://publications.waset.org/abstracts/31912/video-sharing-system-based-on-wi-fi-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31912.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">337</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> Object Recognition System Operating from Different Type Vehicles Using Raspberry and OpenCV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maria%20Pavlova">Maria Pavlova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In our days, it is possible to put the camera on different vehicles like quadcopter, train, airplane and etc. The camera also can be the input sensor in many different systems. That means the object recognition like non separate part of monitoring control can be key part of the most intelligent systems. The aim of this paper is to focus of the object recognition process during vehicles movement. During the vehicle’s movement the camera takes pictures from the environment without storage in Data Base. In case the camera detects a special object (for example human or animal), the system saves the picture and sends it to the work station in real time. This functionality will be very useful in emergency or security situations where is necessary to find a specific object. In another application, the camera can be mounted on crossroad where do not have many people and if one or more persons come on the road, the traffic lights became the green and they can cross the road. In this papers is presented the system has solved the aforementioned problems. It is presented architecture of the object recognition system includes the camera, Raspberry platform, GPS system, neural network, software and Data Base. The camera in the system takes the pictures. The object recognition is done in real time using the OpenCV library and Raspberry microcontroller. An additional feature of this library is the ability to display the GPS coordinates of the captured objects position. The results from this processes will be sent to remote station. So, in this case, we can know the location of the specific object. By neural network, we can learn the module to solve the problems using incoming data and to be part in bigger intelligent system. The present paper focuses on the design and integration of the image recognition like a part of smart systems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera" title="camera">camera</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=OpenCV" title=" OpenCV"> OpenCV</a>, <a href="https://publications.waset.org/abstracts/search?q=Raspberry" title=" Raspberry"> Raspberry</a> </p> <a href="https://publications.waset.org/abstracts/81695/object-recognition-system-operating-from-different-type-vehicles-using-raspberry-and-opencv" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81695.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">218</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> 6D Posture Estimation of Road Vehicles from Color Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yoshimoto%20Kurihara">Yoshimoto Kurihara</a>, <a href="https://publications.waset.org/abstracts/search?q=Tad%20Gonsalves"> Tad Gonsalves</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=6D%20posture%20estimation" title="6D posture estimation">6D posture estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20recognition" title=" image recognition"> image recognition</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=AlexNet" title=" AlexNet"> AlexNet</a> </p> <a href="https://publications.waset.org/abstracts/138449/6d-posture-estimation-of-road-vehicles-from-color-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138449.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">155</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> Maximizing Coverage with Mobile Crime Cameras in a Stochastic Spatiotemporal Bipartite Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=%28Ted%29%20Edward%20Holmberg">(Ted) Edward Holmberg</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Abdelguerfi"> Mahdi Abdelguerfi</a>, <a href="https://publications.waset.org/abstracts/search?q=Elias%20Ioup"> Elias Ioup</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research details a coverage measure for evaluating the effectiveness of observer node placements in a spatial bipartite network. This coverage measure can be used to optimize the configuration of stationary or mobile spatially oriented observer nodes, or a hybrid of the two, over time in order to fully utilize their capabilities. To demonstrate the practical application of this approach, we construct a SpatioTemporal Bipartite Network (STBN) using real-time crime center (RTCC) camera nodes and NOPD calls for service (CFS) event nodes from New Orleans, La (NOLA). We use the coverage measure to identify optimal placements for moving mobile RTCC camera vans to improve coverage of vulnerable areas based on temporal patterns. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=coverage%20measure" title="coverage measure">coverage measure</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20node%20dynamics" title=" mobile node dynamics"> mobile node dynamics</a>, <a href="https://publications.waset.org/abstracts/search?q=Monte%20Carlo%20simulation" title=" Monte Carlo simulation"> Monte Carlo simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=observer%20nodes" title=" observer nodes"> observer nodes</a>, <a href="https://publications.waset.org/abstracts/search?q=observable%20nodes" title=" observable nodes"> observable nodes</a>, <a href="https://publications.waset.org/abstracts/search?q=spatiotemporal%20bipartite%20knowledge%20graph" title=" spatiotemporal bipartite knowledge graph"> spatiotemporal bipartite knowledge graph</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20spatial%20analysis" title=" temporal spatial analysis"> temporal spatial analysis</a> </p> <a href="https://publications.waset.org/abstracts/161229/maximizing-coverage-with-mobile-crime-cameras-in-a-stochastic-spatiotemporal-bipartite-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161229.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">5257</span> Single-Camera Basketball Tracker through Pose and Semantic Feature Fusion</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Adri%C3%A0%20Arbu%C3%A9s-Sang%C3%BCesa">Adrià Arbués-Sangüesa</a>, <a href="https://publications.waset.org/abstracts/search?q=Coloma%20Ballester"> Coloma Ballester</a>, <a href="https://publications.waset.org/abstracts/search?q=Gloria%20Haro"> Gloria Haro</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tracking sports players is a widely challenging scenario, specially in single-feed videos recorded in tight courts, where cluttering and occlusions cannot be avoided. This paper presents an analysis of several geometric and semantic visual features to detect and track basketball players. An ablation study is carried out and then used to remark that a robust tracker can be built with Deep Learning features, without the need of extracting contextual ones, such as proximity or color similarity, nor applying camera stabilization techniques. The presented tracker consists of: (1) a detection step, which uses a pretrained deep learning model to estimate the players pose, followed by (2) a tracking step, which leverages pose and semantic information from the output of a convolutional layer in a VGG network. Its performance is analyzed in terms of MOTA over a basketball dataset with more than 10k instances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=basketball" title="basketball">basketball</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=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=single-camera" title=" single-camera"> single-camera</a>, <a href="https://publications.waset.org/abstracts/search?q=tracking" title=" tracking"> tracking</a> </p> <a href="https://publications.waset.org/abstracts/109446/single-camera-basketball-tracker-through-pose-and-semantic-feature-fusion" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/109446.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">138</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> A Study of Effective Stereo Matching Method for Long-Wave Infrared Camera Module</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyun-Koo%20Kim">Hyun-Koo Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yonghun%20Kim"> Yonghun Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Yong-Hoon%20Kim"> Yong-Hoon Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Ju%20Hee%20Lee"> Ju Hee Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Myungho%20Song"> Myungho Song</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20driver%20assistance%20system" title="advanced driver assistance system">advanced driver assistance system</a>, <a href="https://publications.waset.org/abstracts/search?q=pedestrian%20detection" title=" pedestrian detection"> pedestrian detection</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20matching%20method" title=" stereo matching method"> stereo matching method</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20long-wave%20IR%20camera" title=" stereo long-wave IR camera"> stereo long-wave IR camera</a> </p> <a href="https://publications.waset.org/abstracts/58413/a-study-of-effective-stereo-matching-method-for-long-wave-infrared-camera-module" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58413.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">415</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> Image Features Comparison-Based Position Estimation Method Using a Camera Sensor</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jinseon%20Song">Jinseon Song</a>, <a href="https://publications.waset.org/abstracts/search?q=Yongwan%20Park"> Yongwan Park</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, propose method that can user’s position that based on database is built from single camera. Previous positioning calculate distance by arrival-time of signal like GPS (Global Positioning System), RF(Radio Frequency). However, these previous method have weakness because these have large error range according to signal interference. Method for solution estimate position by camera sensor. But, signal camera is difficult to obtain relative position data and stereo camera is difficult to provide real-time position data because of a lot of image data, too. First of all, in this research we build image database at space that able to provide positioning service with single camera. Next, we judge similarity through image matching of database image and transmission image from user. Finally, we decide position of user through position of most similar database image. For verification of propose method, we experiment at real-environment like indoor and outdoor. Propose method is wide positioning range and this method can verify not only position of user but also direction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=positioning" title="positioning">positioning</a>, <a href="https://publications.waset.org/abstracts/search?q=distance" title=" distance"> distance</a>, <a href="https://publications.waset.org/abstracts/search?q=camera" title=" camera"> camera</a>, <a href="https://publications.waset.org/abstracts/search?q=features" title=" features"> features</a>, <a href="https://publications.waset.org/abstracts/search?q=SURF%28Speed-Up%20Robust%20Features%29" title=" SURF(Speed-Up Robust Features)"> SURF(Speed-Up Robust Features)</a>, <a href="https://publications.waset.org/abstracts/search?q=database" title=" database"> database</a>, <a href="https://publications.waset.org/abstracts/search?q=estimation" title=" estimation"> estimation</a> </p> <a href="https://publications.waset.org/abstracts/11844/image-features-comparison-based-position-estimation-method-using-a-camera-sensor" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/11844.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">350</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> GA3C for Anomalous Radiation Source Detection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia-Yi%20Liu">Chia-Yi Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo-Bin%20Xiao"> Bo-Bin Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=Wen-Bin%20Lin"> Wen-Bin Lin</a>, <a href="https://publications.waset.org/abstracts/search?q=Hsiang-Ning%20Wu"> Hsiang-Ning Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=Liang-Hsun%20Huang"> Liang-Hsun Huang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In order to reduce the risk of radiation damage that personnel may suffer during operations in the radiation environment, the use of automated guided vehicles to assist or replace on-site personnel in the radiation environment has become a key technology and has become an important trend. In this paper, we demonstrate our proof of concept for autonomous self-learning radiation source searcher in an unknown environment without a map. The research uses GPU version of Asynchronous Advantage Actor-Critic network (GA3C) of deep reinforcement learning to search for radiation sources. The searcher network, based on GA3C architecture, has self-directed learned and improved how search the anomalous radiation source by training 1 million episodes under three simulation environments. In each episode of training, the radiation source position, the radiation source intensity, starting position, are all set randomly in one simulation environment. The input for searcher network is the fused data from a 2D laser scanner and a RGB-D camera as well as the value of the radiation detector. The output actions are the linear and angular velocities. The searcher network is trained in a simulation environment to accelerate the learning process. The well-performance searcher network is deployed to the real unmanned vehicle, Dashgo E2, which mounts LIDAR of YDLIDAR G4, RGB-D camera of Intel D455, and radiation detector made by Institute of Nuclear Energy Research. In the field experiment, the unmanned vehicle is enable to search out the radiation source of the 18.5MBq Na-22 by itself and avoid obstacles simultaneously without human interference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title="deep reinforcement learning">deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=GA3C" title=" GA3C"> GA3C</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20searching" title=" source searching"> source searching</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20detection" title=" source detection"> source detection</a> </p> <a href="https://publications.waset.org/abstracts/148264/ga3c-for-anomalous-radiation-source-detection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148264.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">5253</span> Subpixel Corner Detection for Monocular Camera Linear Model Research</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guorong%20Sui">Guorong Sui</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingwei%20Jia"> Xingwei Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Fei%20Tong"> Fei Tong</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiumin%20Gao"> Xiumin Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Camera calibration is a fundamental issue of high precision noncontact measurement. And it is necessary to analyze and study the reliability and application range of its linear model which is often used in the camera calibration. According to the imaging features of monocular cameras, a camera model which is based on the image pixel coordinates and three dimensional space coordinates is built. Using our own customized template, the image pixel coordinate is obtained by the subpixel corner detection method. Without considering the aberration of the optical system, the feature extraction and linearity analysis of the line segment in the template are performed. Moreover, the experiment is repeated 11 times by constantly varying the measuring distance. At last, the linearity of the camera is achieved by fitting 11 groups of data. The camera model measurement results show that the relative error does not exceed 1%, and the repeated measurement error is not more than 0.1 mm magnitude. Meanwhile, it is found that the model has some measurement differences in the different region and object distance. The experiment results show this linear model is simple and practical, and have good linearity within a certain object distance. These experiment results provide a powerful basis for establishment of the linear model of camera. These works will have potential value to the actual engineering measurement. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20linear%20model" title="camera linear model">camera linear model</a>, <a href="https://publications.waset.org/abstracts/search?q=geometric%20imaging%20relationship" title=" geometric imaging relationship"> geometric imaging relationship</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20pixel%20coordinates" title=" image pixel coordinates"> image pixel coordinates</a>, <a href="https://publications.waset.org/abstracts/search?q=three%20dimensional%20space%20coordinates" title=" three dimensional space coordinates"> three dimensional space coordinates</a>, <a href="https://publications.waset.org/abstracts/search?q=sub-pixel%20corner%20detection" title=" sub-pixel corner detection"> sub-pixel corner detection</a> </p> <a href="https://publications.waset.org/abstracts/77747/subpixel-corner-detection-for-monocular-camera-linear-model-research" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77747.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">277</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> X-Corner Detection for Camera Calibration Using Saddle Points</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdulrahman%20S.%20Alturki">Abdulrahman S. Alturki</a>, <a href="https://publications.waset.org/abstracts/search?q=John%20S.%20Loomis"> John S. Loomis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20calibration" title="camera calibration">camera calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=corner%20detector" title=" corner detector"> corner detector</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20detector" title=" edge detector"> edge detector</a>, <a href="https://publications.waset.org/abstracts/search?q=saddle%20points" title=" saddle points"> saddle points</a> </p> <a href="https://publications.waset.org/abstracts/40538/x-corner-detection-for-camera-calibration-using-saddle-points" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40538.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">406</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> Frame Camera and Event Camera in Stereo Pair for High-Resolution Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Khen%20Cohen">Khen Cohen</a>, <a href="https://publications.waset.org/abstracts/search?q=Daniel%20Yankelevich"> Daniel Yankelevich</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Mendlovic"> David Mendlovic</a>, <a href="https://publications.waset.org/abstracts/search?q=Dan%20Raviv"> Dan Raviv</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a 3D stereo system for high-resolution sensing in both the spatial and the temporal domains by combining a frame-based camera and an event-based camera. We establish a method to merge both devices into one unite system and introduce a calibration process, followed by a correspondence technique and interpolation algorithm for 3D reconstruction. We further provide quantitative analysis about our system in terms of depth resolution and additional parameter analysis. We show experimentally how our system performs temporal super-resolution up to effectively 1ms and can detect fast-moving objects and human micro-movements that can be used for micro-expression analysis. We also demonstrate how our method can extract colored events for an event-based camera without any degradation in the spatial resolution, compared to a colored filter array. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DVS-CIS%20stereo%20vision" title="DVS-CIS stereo vision">DVS-CIS stereo vision</a>, <a href="https://publications.waset.org/abstracts/search?q=micro-movements" title=" micro-movements"> micro-movements</a>, <a href="https://publications.waset.org/abstracts/search?q=temporal%20super-resolution" title=" temporal super-resolution"> temporal super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20reconstruction" title=" 3D reconstruction"> 3D reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/143524/frame-camera-and-event-camera-in-stereo-pair-for-high-resolution-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/143524.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">297</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> H.263 Based Video Transceiver for Wireless Camera System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Won-Ho%20Kim">Won-Ho Kim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a design of H.263 based wireless video transceiver is presented for wireless camera system. It uses standard WIFI transceiver and the covering area is up to 100m. Furthermore the standard H.263 video encoding technique is used for video compression since wireless video transmitter is unable to transmit high capacity raw data in real time and the implemented system is capable of streaming at speed of less than 1Mbps using NTSC 720x480 video. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=wireless%20video%20transceiver" title="wireless video transceiver">wireless video transceiver</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance%20camera" title=" video surveillance camera"> video surveillance camera</a>, <a href="https://publications.waset.org/abstracts/search?q=H.263%20video%20encoding%20digital%20signal%20processing" title=" H.263 video encoding digital signal processing"> H.263 video encoding digital signal processing</a> </p> <a href="https://publications.waset.org/abstracts/12951/h263-based-video-transceiver-for-wireless-camera-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12951.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">365</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">5249</span> 3D Plant Growth Measurement System Using Deep Learning Technology</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazuaki%20Shiraishi">Kazuaki Shiraishi</a>, <a href="https://publications.waset.org/abstracts/search?q=Narumitsu%20Asai"> Narumitsu Asai</a>, <a href="https://publications.waset.org/abstracts/search?q=Tsukasa%20Kitahara"> Tsukasa Kitahara</a>, <a href="https://publications.waset.org/abstracts/search?q=Sosuke%20Mieno"> Sosuke Mieno</a>, <a href="https://publications.waset.org/abstracts/search?q=Takaharu%20Kameoka"> Takaharu Kameoka</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this research is to facilitate productivity advances in agriculture. To accomplish this, we developed an automatic three-dimensional (3D) recording system for growth of field crops that consists of a number of inexpensive modules: a very low-cost stereo camera, a couple of ZigBee wireless modules, a Raspberry Pi single-board computer, and a third generation (3G) wireless communication module. Our system uses an inexpensive Web stereo camera in order to keep total costs low. However, inexpensive video cameras record low-resolution images that are very noisy. Accordingly, in order to resolve these problems, we adopted a deep learning method. Based on the results of extended period of time operation test conducted without the use of an external power supply, we found that by using Super-Resolution Convolutional Neural Network method, our system could achieve a balance between the competing goals of low-cost and superior performance. Our experimental results showed the effectiveness of our system. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20plant%20data" title="3D plant data">3D plant data</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20recording" title=" automatic recording"> automatic recording</a>, <a href="https://publications.waset.org/abstracts/search?q=stereo%20camera" title=" stereo camera"> stereo camera</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=image%20processing" title=" image processing"> image processing</a> </p> <a href="https://publications.waset.org/abstracts/54418/3d-plant-growth-measurement-system-using-deep-learning-technology" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54418.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">273</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">5248</span> A Wide View Scheme for Automobile's Black Box</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jaemyoung%20Lee">Jaemyoung Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We propose a wide view camera scheme for automobile's black box. The proposed scheme uses the commercially available camera lenses of which view angles are about 120°}^{\circ}°. In the proposed scheme, we extend the view angle to approximately 200° ^{\circ}° using two cameras at the front side instead of three lenses with conventional black boxes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera" title="camera">camera</a>, <a href="https://publications.waset.org/abstracts/search?q=black%20box" title=" black box"> black box</a>, <a href="https://publications.waset.org/abstracts/search?q=view%20angle" title=" view angle"> view angle</a>, <a href="https://publications.waset.org/abstracts/search?q=automobile" title=" automobile"> automobile</a> </p> <a href="https://publications.waset.org/abstracts/2582/a-wide-view-scheme-for-automobiles-black-box" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2582.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">413</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">5247</span> Modal Analysis of a Cantilever Beam Using an Inexpensive Smartphone Camera: Motion Magnification Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hasan%20Hassoun">Hasan Hassoun</a>, <a href="https://publications.waset.org/abstracts/search?q=Jaafar%20Hallal"> Jaafar Hallal</a>, <a href="https://publications.waset.org/abstracts/search?q=Denis%20Duhamel"> Denis Duhamel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Hammoud"> Mohammad Hammoud</a>, <a href="https://publications.waset.org/abstracts/search?q=Ali%20Hage%20Diab"> Ali Hage Diab</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper aims to prove the accuracy of an inexpensive smartphone camera as a non-contact vibration sensor to recover the vibration modes of a vibrating structure such as a cantilever beam. A video of a vibrating beam is filmed using a smartphone camera and then processed by the motion magnification technique. Based on this method, the first two natural frequencies and their associated mode shapes are estimated experimentally and compared to the analytical ones. Results show a relative error of less than 4% between the experimental and analytical approaches for the first two natural frequencies of the beam. Also, for the first two-mode shapes, a Modal Assurance Criterion (MAC) value of above 0.9 between the two approaches is obtained. This slight error between the different techniques ensures the viability of a cheap smartphone camera as a non-contact vibration sensor, particularly for structures vibrating at relatively low natural frequencies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=modal%20analysis" title="modal analysis">modal analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20magnification" title=" motion magnification"> motion magnification</a>, <a href="https://publications.waset.org/abstracts/search?q=smartphone%20camera" title=" smartphone camera"> smartphone camera</a>, <a href="https://publications.waset.org/abstracts/search?q=structural%20vibration" title=" structural vibration"> structural vibration</a>, <a href="https://publications.waset.org/abstracts/search?q=vibration%20modes" title=" vibration modes"> vibration modes</a> </p> <a href="https://publications.waset.org/abstracts/127525/modal-analysis-of-a-cantilever-beam-using-an-inexpensive-smartphone-camera-motion-magnification-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127525.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">148</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">5246</span> GIS-Based Automatic Flight Planning of Camera-Equipped UAVs for Fire Emergency Response</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mohammed%20Sulaiman">Mohammed Sulaiman</a>, <a href="https://publications.waset.org/abstracts/search?q=Hexu%20Liu"> Hexu Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20Binalhaj"> Mohamed Binalhaj</a>, <a href="https://publications.waset.org/abstracts/search?q=William%20W.%20Liou"> William W. Liou</a>, <a href="https://publications.waset.org/abstracts/search?q=Osama%20Abudayyeh"> Osama Abudayyeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Emerging technologies such as camera-equipped unmanned aerial vehicles (UAVs) are increasingly being applied in building fire rescue to provide real-time visualization and 3D reconstruction of the entire fireground. However, flight planning of camera-equipped UAVs is usually a manual process, which is not sufficient to fulfill the needs of emergency management. This research proposes a Geographic Information System (GIS)-based approach to automatic flight planning of camera-equipped UAVs for building fire emergency response. In this research, Haversine formula and lawn mowing patterns are employed to automate flight planning based on geometrical and spatial information from GIS. The resulting flight mission satisfies the requirements of 3D reconstruction purposes of the fireground, in consideration of flight execution safety and visibility of camera frames. The proposed approach is implemented within a GIS environment through an application programming interface. A case study is used to demonstrate the effectiveness of the proposed approach. The result shows that flight mission can be generated in a timely manner for application to fire emergency response. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=GIS" title="GIS">GIS</a>, <a href="https://publications.waset.org/abstracts/search?q=camera-equipped%20UAVs" title=" camera-equipped UAVs"> camera-equipped UAVs</a>, <a href="https://publications.waset.org/abstracts/search?q=automatic%20flight%20planning" title=" automatic flight planning"> automatic flight planning</a>, <a href="https://publications.waset.org/abstracts/search?q=fire%20emergency%20response" title=" fire emergency response"> fire emergency response</a> </p> <a href="https://publications.waset.org/abstracts/125166/gis-based-automatic-flight-planning-of-camera-equipped-uavs-for-fire-emergency-response" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/125166.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">125</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">5245</span> Hand Gesture Recognition Interface Based on IR Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yang-Keun%20Ahn">Yang-Keun Ahn</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang-Soon%20Choi"> Kwang-Soon Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Young-Choong%20Park"> Young-Choong Park</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang-Mo%20Jung"> Kwang-Mo Jung</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Vision based user interfaces to control TVs and PCs have the advantage of being able to perform natural control without being limited to a specific device. Accordingly, various studies on hand gesture recognition using RGB cameras or depth cameras have been conducted. However, such cameras have the disadvantage of lacking in accuracy or the construction cost being large. The proposed method uses a low cost IR camera to accurately differentiate between the hand and the background. Also, complicated learning and template matching methodologies are not used, and the correlation between the fingertips extracted through curvatures is utilized to recognize Click and Move gestures. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=recognition" title="recognition">recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20gestures" title=" hand gestures"> hand gestures</a>, <a href="https://publications.waset.org/abstracts/search?q=infrared%20camera" title=" infrared camera"> infrared camera</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB%20cameras" title=" RGB cameras"> RGB cameras</a> </p> <a href="https://publications.waset.org/abstracts/13373/hand-gesture-recognition-interface-based-on-ir-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13373.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">406</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">5244</span> An Investigation of Direct and Indirect Geo-Referencing Techniques on the Accuracy of Points in Photogrammetry</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20Yildiz">F. Yildiz</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Y.%20Oturanc"> S. Y. Oturanc</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Advances technology in the field of photogrammetry replaces analog cameras with reflection on aircraft GPS/IMU system with a digital aerial camera. In this system, when determining the position of the camera with the GPS, camera rotations are also determined by the IMU systems. All around the world, digital aerial cameras have been used for the photogrammetry applications in the last ten years. In this way, in terms of the work done in photogrammetry it is possible to use time effectively, costs to be reduced to a minimum level, the opportunity to make fast and accurate. Geo-referencing techniques that are the cornerstone of the GPS / INS systems, photogrammetric triangulation of images required for balancing (interior and exterior orientation) brings flexibility to the process. Also geo-referencing process; needed in the application of photogrammetry targets to help to reduce the number of ground control points. In this study, the use of direct and indirect geo-referencing techniques on the accuracy of the points was investigated in the production of photogrammetric mapping. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=photogrammetry" title="photogrammetry">photogrammetry</a>, <a href="https://publications.waset.org/abstracts/search?q=GPS%2FIMU%20systems" title=" GPS/IMU systems"> GPS/IMU systems</a>, <a href="https://publications.waset.org/abstracts/search?q=geo-referecing" title=" geo-referecing"> geo-referecing</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20aerial%20camera" title=" digital aerial camera"> digital aerial camera</a> </p> <a href="https://publications.waset.org/abstracts/13852/an-investigation-of-direct-and-indirect-geo-referencing-techniques-on-the-accuracy-of-points-in-photogrammetry" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/13852.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">411</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">5243</span> Self-Calibration of Fish-Eye Camera for Advanced Driver Assistance Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Atef%20Alaaeddine%20Sarraj">Atef Alaaeddine Sarraj</a>, <a href="https://publications.waset.org/abstracts/search?q=Brendan%20Jackman"> Brendan Jackman</a>, <a href="https://publications.waset.org/abstracts/search?q=Frank%20Walsh"> Frank Walsh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Tomorrow’s car will be more automated and increasingly connected. Innovative and intuitive interfaces are essential to accompany this functional enrichment. For that, today the automotive companies are competing to offer an advanced driver assistance system (ADAS) which will be able to provide enhanced navigation, collision avoidance, intersection support and lane keeping. These vision-based functions require an accurately calibrated camera. To achieve such differentiation in ADAS requires sophisticated sensors and efficient algorithms. This paper explores the different calibration methods applicable to vehicle-mounted fish-eye cameras with arbitrary fields of view and defines the first steps towards a self-calibration method that adequately addresses ADAS requirements. In particular, we present a self-calibration method after comparing different camera calibration algorithms in the context of ADAS requirements. Our method gathers data from unknown scenes while the car is moving, estimates the camera intrinsic and extrinsic parameters and corrects the wide-angle distortion. Our solution enables continuous and real-time detection of objects, pedestrians, road markings and other cars. In contrast, other camera calibration algorithms for ADAS need pre-calibration, while the presented method calibrates the camera without prior knowledge of the scene and in real-time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=advanced%20driver%20assistance%20system%20%28ADAS%29" title="advanced driver assistance system (ADAS)">advanced driver assistance system (ADAS)</a>, <a href="https://publications.waset.org/abstracts/search?q=fish-eye" title=" fish-eye"> fish-eye</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time" title=" real-time"> real-time</a>, <a href="https://publications.waset.org/abstracts/search?q=self-calibration" title=" self-calibration"> self-calibration</a> </p> <a href="https://publications.waset.org/abstracts/70853/self-calibration-of-fish-eye-camera-for-advanced-driver-assistance-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70853.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">252</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">5242</span> Human Identification and Detection of Suspicious Incidents Based on Outfit Colors: Image Processing Approach in CCTV Videos</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thilini%20M.%20Yatanwala">Thilini M. Yatanwala</a> </p> <p class="card-text"><strong>Abstract:</strong></p> CCTV (Closed-Circuit-Television) Surveillance System is being used in public places over decades and a large variety of data is being produced every moment. However, most of the CCTV data is stored in isolation without having integrity. As a result, identification of the behavior of suspicious people along with their location has become strenuous. This research was conducted to acquire more accurate and reliable timely information from the CCTV video records. The implemented system can identify human objects in public places based on outfit colors. Inter-process communication technologies were used to implement the CCTV camera network to track people in the premises. The research was conducted in three stages and in the first stage human objects were filtered from other movable objects available in public places. In the second stage people were uniquely identified based on their outfit colors and in the third stage an individual was continuously tracked in the CCTV network. A face detection algorithm was implemented using cascade classifier based on the training model to detect human objects. HAAR feature based two-dimensional convolution operator was introduced to identify features of the human face such as region of eyes, region of nose and bridge of the nose based on darkness and lightness of facial area. In the second stage outfit colors of human objects were analyzed by dividing the area into upper left, upper right, lower left, lower right of the body. Mean color, mod color and standard deviation of each area were extracted as crucial factors to uniquely identify human object using histogram based approach. Color based measurements were written in to XML files and separate directories were maintained to store XML files related to each camera according to time stamp. As the third stage of the approach, inter-process communication techniques were used to implement an acknowledgement based CCTV camera network to continuously track individuals in a network of cameras. Real time analysis of XML files generated in each camera can determine the path of individual to monitor full activity sequence. Higher efficiency was achieved by sending and receiving acknowledgments only among adjacent cameras. Suspicious incidents such as a person staying in a sensitive area for a longer period or a person disappeared from the camera coverage can be detected in this approach. The system was tested for 150 people with the accuracy level of 82%. However, this approach was unable to produce expected results in the presence of group of people wearing similar type of outfits. This approach can be applied to any existing camera network without changing the physical arrangement of CCTV cameras. The study of human identification and suspicious incident detection using outfit color analysis can achieve higher level of accuracy and the project will be continued by integrating motion and gait feature analysis techniques to derive more information from CCTV videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CCTV%20surveillance" title="CCTV surveillance">CCTV surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20detection%20and%20identification" title=" human detection and identification"> human detection and identification</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=inter-process%20communication" title=" inter-process communication"> inter-process communication</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=suspicious%20detection" title=" suspicious detection"> suspicious detection</a> </p> <a href="https://publications.waset.org/abstracts/75863/human-identification-and-detection-of-suspicious-incidents-based-on-outfit-colors-image-processing-approach-in-cctv-videos" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75863.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">183</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">5241</span> A Simple Autonomous Hovering and Operating Control of Multicopter Using Only Web Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kazuya%20Sato">Kazuya Sato</a>, <a href="https://publications.waset.org/abstracts/search?q=Toru%20Kasahara"> Toru Kasahara</a>, <a href="https://publications.waset.org/abstracts/search?q=Junji%20Kuroda"> Junji Kuroda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, an autonomous hovering control method of multicopter using only Web camera is proposed. Recently, various control method of an autonomous flight for multicopter are proposed. But, in the previously proposed methods, a motion capture system (i.e., OptiTrack) and laser range finder are often used to measure the position and posture of multicopter. To achieve an autonomous flight control of multicopter with simple equipment, we propose an autonomous flight control method using AR marker and Web camera. AR marker can measure the position of multicopter with Cartesian coordinate in three dimensional, then its position connects with aileron, elevator, and accelerator throttle operation. A simple PID control method is applied to the each operation and adjust the controller gains. Experimental result are given to show the effectiveness of our proposed method. Moreover, another simple operation method for autonomous flight control multicopter is also proposed. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20hovering%20control" title="autonomous hovering control">autonomous hovering control</a>, <a href="https://publications.waset.org/abstracts/search?q=multicopter" title=" multicopter"> multicopter</a>, <a href="https://publications.waset.org/abstracts/search?q=Web%20camera" title=" Web camera"> Web camera</a>, <a href="https://publications.waset.org/abstracts/search?q=operation" title=" operation "> operation </a> </p> <a href="https://publications.waset.org/abstracts/20333/a-simple-autonomous-hovering-and-operating-control-of-multicopter-using-only-web-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/20333.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">562</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">5240</span> Smart Side View Mirror Camera for Real Time System</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nunziata%20Ivana%20Guarneri">Nunziata Ivana Guarneri</a>, <a href="https://publications.waset.org/abstracts/search?q=Arcangelo%20Bruna"> Arcangelo Bruna</a>, <a href="https://publications.waset.org/abstracts/search?q=Giuseppe%20Spampinato"> Giuseppe Spampinato</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonio%20Buemi"> Antonio Buemi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the last decade, automotive companies have invested a lot in terms of innovation about many aspects regarding the automatic driver assistance systems. One innovation regards the usage of a smart camera placed on the car’s side mirror for monitoring the back and lateral road situation. A common road scenario is the overtaking of the preceding car and, in this case, a brief distraction or a loss of concentration can lead the driver to undertake this action, even if there is an already overtaking vehicle, leading to serious accidents. A valid support for a secure drive can be a smart camera system, which is able to automatically analyze the road scenario and consequentially to warn the driver when another vehicle is overtaking. This paper describes a method for monitoring the side view of a vehicle by using camera optical flow motion vectors. The proposed solution detects the presence of incoming vehicles, assesses their distance from the host car, and warns the driver through different levels of alert according to the estimated distance. Due to the low complexity and computational cost, the proposed system ensures real time performances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20calibration" title="camera calibration">camera calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=ego-motion" title=" ego-motion"> ego-motion</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filters" title=" Kalman filters"> Kalman filters</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=real%20time%20systems" title=" real time systems"> real time systems</a> </p> <a href="https://publications.waset.org/abstracts/79998/smart-side-view-mirror-camera-for-real-time-system" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/79998.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">228</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">5239</span> Multiplayer RC-car Driving System in a Collaborative Augmented Reality Environment</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kikuo%20Asai">Kikuo Asai</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuji%20Sugimoto"> Yuji Sugimoto</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We developed a prototype system for multiplayer RC-car driving in a collaborative Augmented Reality (AR) environment. The tele-existence environment is constructed by superimposing digital data onto images captured by a camera on an RC-car, enabling players to experience an augmented coexistence of the digital content and the real world. Marker-based tracking was used for estimating position and orientation of the camera. The plural RC-cars can be operated in a field where square markers are arranged. The video images captured by the camera are transmitted to a PC for visual tracking. The RC-cars are also tracked by using an infrared camera attached to the ceiling, so that the instability is reduced in the visual tracking. Multimedia data such as texts and graphics are visualized to be overlaid onto the video images in the geometrically correct manner. The prototype system allows a tele-existence sensation to be augmented in a collaborative AR environment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multiplayer" title="multiplayer">multiplayer</a>, <a href="https://publications.waset.org/abstracts/search?q=RC-car" title=" RC-car"> RC-car</a>, <a href="https://publications.waset.org/abstracts/search?q=collaborative%20environment" title=" collaborative environment"> collaborative environment</a>, <a href="https://publications.waset.org/abstracts/search?q=augmented%20reality" title=" augmented reality"> augmented reality</a> </p> <a href="https://publications.waset.org/abstracts/4359/multiplayer-rc-car-driving-system-in-a-collaborative-augmented-reality-environment" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/4359.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">289</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">5238</span> Improvement of Camera Calibration Based on the Relationship between Focal Length and Aberration Coefficient</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Guorong%20Sui">Guorong Sui</a>, <a href="https://publications.waset.org/abstracts/search?q=Xingwei%20Jia"> Xingwei Jia</a>, <a href="https://publications.waset.org/abstracts/search?q=Chenhui%20Yin"> Chenhui Yin</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiumin%20Gao"> Xiumin Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the processing of camera-based high precision and non-contact measurement, the geometric-optical aberration is always inevitably disturbing the measuring system. Moreover, the aberration is different with the different focal length, which will increase the difficulties of the system’s calibration. Therefore, to understand the relationship between the focal length as a function of aberration properties is a very important issue to the calibration of the measuring systems. In this study, we propose a new mathematics model, which is based on the plane calibration method by Zhang Zhengyou, and establish a relationship between the focal length and aberration coefficient. By using the mathematics model and carefully modified compensation templates, the calibration precision of the system can be dramatically improved. The experiment results show that the relative error is less than 1%. It is important for optoelectronic imaging systems that apply to measure, track and position by changing the camera’s focal length. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20calibration" title="camera calibration">camera calibration</a>, <a href="https://publications.waset.org/abstracts/search?q=aberration%20coefficient" title=" aberration coefficient"> aberration coefficient</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20measurement" title=" vision measurement"> vision measurement</a>, <a href="https://publications.waset.org/abstracts/search?q=focal%20length" title=" focal length"> focal length</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematics%20model" title=" mathematics model"> mathematics model</a> </p> <a href="https://publications.waset.org/abstracts/77749/improvement-of-camera-calibration-based-on-the-relationship-between-focal-length-and-aberration-coefficient" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77749.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">364</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">5237</span> Analysis and Control of Camera Type Weft Straightener</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jae-Yong%20Lee">Jae-Yong Lee</a>, <a href="https://publications.waset.org/abstracts/search?q=Gyu-Hyun%20Bae"> Gyu-Hyun Bae</a>, <a href="https://publications.waset.org/abstracts/search?q=Yun-Soo%20Chung"> Yun-Soo Chung</a>, <a href="https://publications.waset.org/abstracts/search?q=Dae-Sub%20Kim"> Dae-Sub Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Jae-Sung%20Bae"> Jae-Sung Bae</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In general, fabric is heat-treated using a stenter machine in order to dry and fix its shape. It is important to shape before the heat treatment because it is difficult to revert back once the fabric is formed. To produce the product of right shape, camera type weft straightener has been applied recently to capture and process fabric images quickly. It is more powerful in determining the final textile quality rather than photo-sensor. Positioning in front of a stenter machine, weft straightener helps to spread fabric evenly and control the angle between warp and weft constantly as right angle by handling skew and bow rollers. To process this tricky procedure, the structural analysis should be carried out in advance, based on which, its control technology can be drawn. A structural analysis is to figure out the specific contact/slippage characteristics between fabric and roller. We already examined the applicability of camera type weft straightener to plain weave fabric and found its possibility and the specific working condition of machine and rollers. In this research, we aimed to explore another applicability of camera type weft straightener. Namely, we tried to figure out camera type weft straightener can be used for fabrics. To find out the optimum condition, we increased the number of rollers. The analysis is done by ANSYS software using Finite Element Analysis method. The control function is demonstrated by experiment. In conclusion, the structural analysis of weft straightener is done to identify a specific characteristic between roller and fabrics. The control of skew and bow roller is done to decrease the error of the angle between warp and weft. Finally, it is proved that camera type straightener can also be used for the special fabrics. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=camera%20type%20weft%20straightener" title="camera type weft straightener">camera type weft straightener</a>, <a href="https://publications.waset.org/abstracts/search?q=structure%20analysis" title=" structure analysis"> structure analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=control" title=" control"> control</a>, <a href="https://publications.waset.org/abstracts/search?q=skew%20and%20bow%20roller" title=" skew and bow roller"> skew and bow roller</a> </p> <a href="https://publications.waset.org/abstracts/58316/analysis-and-control-of-camera-type-weft-straightener" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/58316.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">292</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">5236</span> Development of Intelligent Construction Management System Using Web-Camera Image and 3D Object Image</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hyeon-Seung%20Kim">Hyeon-Seung Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Bit-Na%20Cho"> Bit-Na Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Tae-Woon%20Jeong"> Tae-Woon Jeong</a>, <a href="https://publications.waset.org/abstracts/search?q=Soo-Young%20Yoon"> Soo-Young Yoon</a>, <a href="https://publications.waset.org/abstracts/search?q=Leen-Seok%20Kang"> Leen-Seok Kang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Recently, a construction project has been large in the size and complicated in the site work. The web-cameras are used to manage the construction site of such a large construction project. They can be used for monitoring the construction schedule as compared to the actual work image of the planned work schedule. Specially, because the 4D CAD system that the construction appearance is continually simulated in a 3D CAD object by work schedule is widely applied to the construction project, the comparison system between the real image of actual work appearance by web-camera and the simulated image of planned work appearance by 3D CAD object can be an intelligent construction schedule management system (ICON). The delayed activities comparing with the planned schedule can be simulated by red color in the ICON as a virtual reality object. This study developed the ICON and it was verified in a real bridge construction project in Korea. To verify the developed system, a web-camera was installed and operated in a case project for a month. Because the angle and zooming of the web-camera can be operated by Internet, a project manager can easily monitor and assume the corrective action. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=4D%20CAD" title="4D CAD">4D CAD</a>, <a href="https://publications.waset.org/abstracts/search?q=web-camera" title=" web-camera"> web-camera</a>, <a href="https://publications.waset.org/abstracts/search?q=ICON%20%28intelligent%20construction%20schedule%20management%20system%29" title=" ICON (intelligent construction schedule management system)"> ICON (intelligent construction schedule management system)</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20object%20image" title=" 3D object image"> 3D object image</a> </p> <a href="https://publications.waset.org/abstracts/18621/development-of-intelligent-construction-management-system-using-web-camera-image-and-3d-object-image" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18621.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">507</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">5235</span> Inspection of Railway Track Fastening Elements Using Artificial Vision</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdelkrim%20Belhaoua">Abdelkrim Belhaoua</a>, <a href="https://publications.waset.org/abstracts/search?q=Jean-Pierre%20Radoux"> Jean-Pierre Radoux</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In France, the railway network is one of the main transport infrastructures and is the second largest European network. Therefore, railway inspection is an important task in railway maintenance to ensure safety for passengers using significant means in personal and technical facilities. Artificial vision has recently been applied to several railway applications due to its potential to improve the efficiency and accuracy when analyzing large databases of acquired images. In this paper, we present a vision system able to detect fastening elements based on artificial vision approach. This system acquires railway images using a CCD camera installed under a control carriage. These images are stitched together before having processed. Experimental results are presented to show that the proposed method is robust for detection fasteners in a complex environment. <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=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=railway%20inspection" title=" railway inspection"> railway inspection</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20stitching" title=" image stitching"> image stitching</a>, <a href="https://publications.waset.org/abstracts/search?q=fastener%20recognition" title=" fastener recognition"> fastener recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=neural%20network" title=" neural network"> neural network</a> </p> <a href="https://publications.waset.org/abstracts/38749/inspection-of-railway-track-fastening-elements-using-artificial-vision" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/38749.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">455</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">5234</span> Detecting and Disabling Digital Cameras Using D3CIP Algorithm Based on Image Processing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Vignesh">S. Vignesh</a>, <a href="https://publications.waset.org/abstracts/search?q=K.%20S.%20Rangasamy"> K. S. Rangasamy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper deals with the device capable of detecting and disabling digital cameras. The system locates the camera and then neutralizes it. Every digital camera has an image sensor known as a CCD, which is retro-reflective and sends light back directly to its original source at the same angle. The device shines infrared LED light, which is invisible to the human eye, at a distance of about 20 feet. It then collects video of these reflections with a camcorder. Then the video of the reflections is transferred to a computer connected to the device, where it is sent through image processing algorithms that pick out infrared light bouncing back. Once the camera is detected, the device would project an invisible infrared laser into the camera's lens, thereby overexposing the photo and rendering it useless. Low levels of infrared laser neutralize digital cameras but are neither a health danger to humans nor a physical damage to cameras. We also discuss the simplified design of the above device that can used in theatres to prevent piracy. The domains being covered here are optics and image processing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CCD" title="CCD">CCD</a>, <a href="https://publications.waset.org/abstracts/search?q=optics" title=" optics"> optics</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=D3CIP" title=" D3CIP"> D3CIP</a> </p> <a href="https://publications.waset.org/abstracts/1736/detecting-and-disabling-digital-cameras-using-d3cip-algorithm-based-on-image-processing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1736.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">357</span> </span> </div> </div> <ul class="pagination"> <li 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