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Source Identification</title> <meta name="description" content="Search results for: Camera Source Identification"> <meta name="keywords" content="Camera Source Identification"> <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> 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Camera Source Identification"> <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> 7863</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Camera Source Identification</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7863</span> Evaluation of Sensor Pattern Noise Estimators for Source Camera Identification </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Benjamin%20Anderson-Sackaney">Benjamin Anderson-Sackaney</a>, <a href="https://publications.waset.org/abstracts/search?q=Amr%20Abdel-Dayem"> Amr Abdel-Dayem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper presents a comprehensive survey of recent source camera identification (SCI) systems. Then, the performance of various sensor pattern noise (SPN) estimators was experimentally assessed, under common photo response non-uniformity (PRNU) frameworks. The experiments used 1350 natural and 900 flat-field images, captured by 18 individual cameras. 12 different experiments, grouped into three sets, were conducted. The results were analyzed using the receiver operator characteristic (ROC) curves. The experimental results demonstrated that combining the basic SPN estimator with a wavelet-based filtering scheme provides promising results. However, the phase SPN estimator fits better with both patch-based (BM3D) and anisotropic diffusion (AD) filtering schemes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=sensor%20pattern%20noise" title="sensor pattern noise">sensor pattern noise</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20camera%20identification" title=" source camera identification"> source camera identification</a>, <a href="https://publications.waset.org/abstracts/search?q=photo%20response%20non-uniformity" title=" photo response non-uniformity"> photo response non-uniformity</a>, <a href="https://publications.waset.org/abstracts/search?q=anisotropic%20diffusion" title=" anisotropic diffusion"> anisotropic diffusion</a>, <a href="https://publications.waset.org/abstracts/search?q=peak%20to%20correlation%20energy%20ratio" title=" peak to correlation energy ratio"> peak to correlation energy ratio</a> </p> <a href="https://publications.waset.org/abstracts/63183/evaluation-of-sensor-pattern-noise-estimators-for-source-camera-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/63183.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">441</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">7862</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">72</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">7861</span> Forensic Challenges in Source Device Identification for Digital Videos</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mustapha%20Aminu%20Bagiwa">Mustapha Aminu Bagiwa</a>, <a href="https://publications.waset.org/abstracts/search?q=Ainuddin%20Wahid%20Abdul%20Wahab"> Ainuddin Wahid Abdul Wahab</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohd%20Yamani%20Idna%20Idris"> Mohd Yamani Idna Idris</a>, <a href="https://publications.waset.org/abstracts/search?q=Suleman%20Khan"> Suleman Khan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video source device identification has become a problem of concern in numerous domains especially in multimedia security and digital investigation. This is because videos are now used as evidence in legal proceedings. Source device identification aim at identifying the source of digital devices using the content they produced. However, due to affordable processing tools and the influx in digital content generating devices, source device identification is still a major problem within the digital forensic community. In this paper, we discuss source device identification for digital videos by identifying techniques that were proposed in the literature for model or specific device identification. This is aimed at identifying salient open challenges for future research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20forgery" title="video forgery">video forgery</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20camcorder" title=" source camcorder"> source camcorder</a>, <a href="https://publications.waset.org/abstracts/search?q=device%20identification" title=" device identification"> device identification</a>, <a href="https://publications.waset.org/abstracts/search?q=forgery%20detection" title=" forgery detection "> forgery detection </a> </p> <a href="https://publications.waset.org/abstracts/21641/forensic-challenges-in-source-device-identification-for-digital-videos" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21641.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">631</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">7860</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">7859</span> Improved Rare Species Identification Using Focal Loss Based Deep Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chad%20Goldsworthy">Chad Goldsworthy</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20Rajeswari%20Matam"> B. Rajeswari Matam</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The use of deep learning for species identification in camera trap images has revolutionised our ability to study, conserve and monitor species in a highly efficient and unobtrusive manner, with state-of-the-art models achieving accuracies surpassing the accuracy of manual human classification. The high imbalance of camera trap datasets, however, results in poor accuracies for minority (rare or endangered) species due to their relative insignificance to the overall model accuracy. This paper investigates the use of Focal Loss, in comparison to the traditional Cross Entropy Loss function, to improve the identification of minority species in the “255 Bird Species” dataset from Kaggle. The results show that, although Focal Loss slightly decreased the accuracy of the majority species, it was able to increase the F1-score by 0.06 and improve the identification of the bottom two, five and ten (minority) species by 37.5%, 15.7% and 10.8%, respectively, as well as resulting in an improved overall accuracy of 2.96%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=convolutional%20neural%20networks" title="convolutional neural networks">convolutional neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20imbalance" title=" data imbalance"> data imbalance</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=focal%20loss" title=" focal loss"> focal loss</a>, <a href="https://publications.waset.org/abstracts/search?q=species%20classification" title=" species classification"> species classification</a>, <a href="https://publications.waset.org/abstracts/search?q=wildlife%20conservation" title=" wildlife conservation"> wildlife conservation</a> </p> <a href="https://publications.waset.org/abstracts/132442/improved-rare-species-identification-using-focal-loss-based-deep-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/132442.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">191</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">7858</span> Evaluation of a Data Fusion Algorithm for Detecting and Locating a Radioactive Source through Monte Carlo N-Particle Code Simulation and Experimental Measurement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hadi%20Ardiny">Hadi Ardiny</a>, <a href="https://publications.waset.org/abstracts/search?q=Amir%20Mohammad%20Beigzadeh"> Amir Mohammad Beigzadeh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Through the utilization of a combination of various sensors and data fusion methods, the detection of potential nuclear threats can be significantly enhanced by extracting more information from different data. In this research, an experimental and modeling approach was employed to track a radioactive source by combining a surveillance camera and a radiation detector (NaI). To run this experiment, three mobile robots were utilized, with one of them equipped with a radioactive source. An algorithm was developed in identifying the contaminated robot through correlation between camera images and camera data. The computer vision method extracts the movements of all robots in the XY plane coordinate system, and the detector system records the gamma-ray count. The position of the robots and the corresponding count of the moving source were modeled using the MCNPX simulation code while considering the experimental geometry. The results demonstrated a high level of accuracy in finding and locating the target in both the simulation model and experimental measurement. The modeling techniques prove to be valuable in designing different scenarios and intelligent systems before initiating any experiments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=nuclear%20threats" title="nuclear threats">nuclear threats</a>, <a href="https://publications.waset.org/abstracts/search?q=radiation%20detector" title=" radiation detector"> radiation detector</a>, <a href="https://publications.waset.org/abstracts/search?q=MCNPX%20simulation" title=" MCNPX simulation"> MCNPX simulation</a>, <a href="https://publications.waset.org/abstracts/search?q=modeling%20techniques" title=" modeling techniques"> modeling techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=intelligent%20systems" title=" intelligent systems"> intelligent systems</a> </p> <a href="https://publications.waset.org/abstracts/167591/evaluation-of-a-data-fusion-algorithm-for-detecting-and-locating-a-radioactive-source-through-monte-carlo-n-particle-code-simulation-and-experimental-measurement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/167591.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">123</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">7857</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">7856</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> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7855</span> Automatic Identification and Monitoring of Wildlife via Computer Vision and IoT</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Bilal%20Arshad">Bilal Arshad</a>, <a href="https://publications.waset.org/abstracts/search?q=Johan%20Barthelemy"> Johan Barthelemy</a>, <a href="https://publications.waset.org/abstracts/search?q=Elliott%20Pilton"> Elliott Pilton</a>, <a href="https://publications.waset.org/abstracts/search?q=Pascal%20Perez"> Pascal Perez</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Getting reliable, informative, and up-to-date information about the location, mobility, and behavioural patterns of animals will enhance our ability to research and preserve biodiversity. The fusion of infra-red sensors and camera traps offers an inexpensive way to collect wildlife data in the form of images. However, extracting useful data from these images, such as the identification and counting of animals remains a manual, time-consuming, and costly process. In this paper, we demonstrate that such information can be automatically retrieved by using state-of-the-art deep learning methods. Another major challenge that ecologists are facing is the recounting of one single animal multiple times due to that animal reappearing in other images taken by the same or other camera traps. Nonetheless, such information can be extremely useful for tracking wildlife and understanding its behaviour. To tackle the multiple count problem, we have designed a meshed network of camera traps, so they can share the captured images along with timestamps, cumulative counts, and dimensions of the animal. The proposed method takes leverage of edge computing to support real-time tracking and monitoring of wildlife. This method has been validated in the field and can be easily extended to other applications focusing on wildlife monitoring and management, where the traditional way of monitoring is expensive and time-consuming. <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=ecology" title=" ecology"> ecology</a>, <a href="https://publications.waset.org/abstracts/search?q=internet%20of%20things" title=" internet of things"> internet of things</a>, <a href="https://publications.waset.org/abstracts/search?q=invasive%20species%20management" title=" invasive species management"> invasive species management</a>, <a href="https://publications.waset.org/abstracts/search?q=wildlife%20management" title=" wildlife management"> wildlife management</a> </p> <a href="https://publications.waset.org/abstracts/115450/automatic-identification-and-monitoring-of-wildlife-via-computer-vision-and-iot" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/115450.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">7854</span> Temperature-Based Detection of Initial Yielding Point in Loading of Tensile Specimens Made of Structural Steel</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Aqsa%20Jamil">Aqsa Jamil</a>, <a href="https://publications.waset.org/abstracts/search?q=Tamura%20Hiroshi"> Tamura Hiroshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Katsuchi%20Hiroshi"> Katsuchi Hiroshi</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Jiaqi"> Wang Jiaqi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The yield point represents the upper limit of forces which can be applied to a specimen without causing any permanent deformation. After yielding, the behavior of the specimen suddenly changes, including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of a thermography camera. The yield point of specimens was estimated with the help of temperature dip, which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing a repeatability analysis. The effects of temperature imperfection and light source have been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of the thermographic technique. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=signal%20to%20noise%20ratio" title="signal to noise ratio">signal to noise ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=thermoelastic%20effect" title=" thermoelastic effect"> thermoelastic effect</a>, <a href="https://publications.waset.org/abstracts/search?q=thermography" title=" thermography"> thermography</a>, <a href="https://publications.waset.org/abstracts/search?q=yield%20point" title=" yield point"> yield point</a> </p> <a href="https://publications.waset.org/abstracts/151454/temperature-based-detection-of-initial-yielding-point-in-loading-of-tensile-specimens-made-of-structural-steel" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151454.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">107</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">7853</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">414</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">7852</span> Camera Model Identification for Mi Pad 4, Oppo A37f, Samsung M20, and Oppo f9</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ulrich%20Wake">Ulrich Wake</a>, <a href="https://publications.waset.org/abstracts/search?q=Eniman%20Syamsuddin"> Eniman Syamsuddin</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The model for camera model identificaiton is trained using pretrained model ResNet43 and ResNet50. The dataset consists of 500 photos of each phone. Dataset is divided into 1280 photos for training, 320 photos for validation and 400 photos for testing. The model is trained using One Cycle Policy Method and tested using Test-Time Augmentation. Furthermore, the model is trained for 50 epoch using regularization such as drop out and early stopping. The result is 90% accuracy for validation set and above 85% for Test-Time Augmentation using ResNet50. Every model is also trained by slightly updating the pretrained model’s weights <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=%E2%80%8B%20One%20Cycle%20Policy" title=" One Cycle Policy"> One Cycle Policy</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet34" title=" ResNet34"> ResNet34</a>, <a href="https://publications.waset.org/abstracts/search?q=ResNet50" title=" ResNet50"> ResNet50</a>, <a href="https://publications.waset.org/abstracts/search?q=Test-Time%20Agumentation" title=" Test-Time Agumentation"> Test-Time Agumentation</a> </p> <a href="https://publications.waset.org/abstracts/124445/camera-model-identification-for-mi-pad-4-oppo-a37f-samsung-m20-and-oppo-f9" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/124445.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">208</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">7851</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">349</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">7850</span> Digital Image Forensics: Discovering the History of Digital Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Gurinder%20Singh">Gurinder Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Kulbir%20Singh"> Kulbir Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Digital multimedia contents such as image, video, and audio can be tampered easily due to the availability of powerful editing softwares. Multimedia forensics is devoted to analyze these contents by using various digital forensic techniques in order to validate their authenticity. Digital image forensics is dedicated to investigate the reliability of digital images by analyzing the integrity of data and by reconstructing the historical information of an image related to its acquisition phase. In this paper, a survey is carried out on the forgery detection by considering the most recent and promising digital image forensic techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Computer%20Forensics" title="Computer Forensics">Computer Forensics</a>, <a href="https://publications.waset.org/abstracts/search?q=Multimedia%20Forensics" title=" Multimedia Forensics"> Multimedia Forensics</a>, <a href="https://publications.waset.org/abstracts/search?q=Image%20Ballistics" title=" Image Ballistics"> Image Ballistics</a>, <a href="https://publications.waset.org/abstracts/search?q=Camera%20Source%20Identification" title=" Camera Source Identification"> Camera Source Identification</a>, <a href="https://publications.waset.org/abstracts/search?q=Forgery%20Detection" title=" Forgery Detection"> Forgery Detection</a> </p> <a href="https://publications.waset.org/abstracts/76669/digital-image-forensics-discovering-the-history-of-digital-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/76669.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">247</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">7849</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">7848</span> An Overview on the Effectiveness of Brand Mascot and Celebrity Endorsement</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Isari%20Pairoa">Isari Pairoa</a>, <a href="https://publications.waset.org/abstracts/search?q=Proud%20Arunrangsiwed"> Proud Arunrangsiwed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Celebrity and brand mascot endorsement have been explored for more than three decades. Both endorsers can effectively transfer their reputation to corporate image and can influence the customers to purchase the product. However, there was little known about the mediators between the level of endorsement and its effect on buying behavior. The objective of the current study is to identify the gab of the previous studies and to seek possible mediators. It was found that consumer’s memory and identification are the mediators, of source credibility and endorsement effect. A future study should confirm the model of endorsement, which was established in the current study. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=product%20endorsement" title="product endorsement">product endorsement</a>, <a href="https://publications.waset.org/abstracts/search?q=memory" title=" memory"> memory</a>, <a href="https://publications.waset.org/abstracts/search?q=identification%20theory" title=" identification theory"> identification theory</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20credibility" title=" source credibility"> source credibility</a>, <a href="https://publications.waset.org/abstracts/search?q=unintentional%20effect" title=" unintentional effect"> unintentional effect</a> </p> <a href="https://publications.waset.org/abstracts/55236/an-overview-on-the-effectiveness-of-brand-mascot-and-celebrity-endorsement" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/55236.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">227</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">7847</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">7846</span> Smartphone Video Source Identification Based on Sensor Pattern Noise</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Raquel%20Ramos%20L%C3%B3pez">Raquel Ramos López</a>, <a href="https://publications.waset.org/abstracts/search?q=Anissa%20El-Khattabi"> Anissa El-Khattabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Ana%20Lucila%20Sandoval%20Orozco"> Ana Lucila Sandoval Orozco</a>, <a href="https://publications.waset.org/abstracts/search?q=Luis%20Javier%20Garc%C3%ADa%20Villalba"> Luis Javier García Villalba</a> </p> <p class="card-text"><strong>Abstract:</strong></p> An increasing number of mobile devices with integrated cameras has meant that most digital video comes from these devices. These digital videos can be made anytime, anywhere and for different purposes. They can also be shared on the Internet in a short period of time and may sometimes contain recordings of illegal acts. The need to reliably trace the origin becomes evident when these videos are used for forensic purposes. This work proposes an algorithm to identify the brand and model of mobile device which generated the video. Its procedure is as follows: after obtaining the relevant video information, a classification algorithm based on sensor noise and Wavelet Transform performs the aforementioned identification process. We also present experimental results that support the validity of the techniques used and show promising results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20video" title="digital video">digital video</a>, <a href="https://publications.waset.org/abstracts/search?q=forensics%20analysis" title=" forensics analysis"> forensics analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20frame" title=" key frame"> key frame</a>, <a href="https://publications.waset.org/abstracts/search?q=mobile%20device" title=" mobile device"> mobile device</a>, <a href="https://publications.waset.org/abstracts/search?q=PRNU" title=" PRNU"> PRNU</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20noise" title=" sensor noise"> sensor noise</a>, <a href="https://publications.waset.org/abstracts/search?q=source%20identification" title=" source identification"> source identification</a> </p> <a href="https://publications.waset.org/abstracts/70332/smartphone-video-source-identification-based-on-sensor-pattern-noise" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70332.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">428</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">7845</span> Depth Camera Aided Dead-Reckoning Localization of Autonomous Mobile Robots in Unstructured GNSS-Denied Environments</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=David%20L.%20Olson">David L. Olson</a>, <a href="https://publications.waset.org/abstracts/search?q=Stephen%20B.%20H.%20Bruder"> Stephen B. H. Bruder</a>, <a href="https://publications.waset.org/abstracts/search?q=Adam%20S.%20Watkins"> Adam S. Watkins</a>, <a href="https://publications.waset.org/abstracts/search?q=Cleon%20E.%20Davis"> Cleon E. Davis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In global navigation satellite systems (GNSS), denied settings such as indoor environments, autonomous mobile robots are often limited to dead-reckoning navigation techniques to determine their position, velocity, and attitude (PVA). Localization is typically accomplished by employing an inertial measurement unit (IMU), which, while precise in nature, accumulates errors rapidly and severely degrades the localization solution. Standard sensor fusion methods, such as Kalman filtering, aim to fuse precise IMU measurements with accurate aiding sensors to establish a precise and accurate solution. In indoor environments, where GNSS and no other a priori information is known about the environment, effective sensor fusion is difficult to achieve, as accurate aiding sensor choices are sparse. However, an opportunity arises by employing a depth camera in the indoor environment. A depth camera can capture point clouds of the surrounding floors and walls. Extracting attitude from these surfaces can serve as an accurate aiding source, which directly combats errors that arise due to gyroscope imperfections. This configuration for sensor fusion leads to a dramatic reduction of PVA error compared to traditional aiding sensor configurations. This paper provides the theoretical basis for the depth camera aiding sensor method, initial expectations of performance benefit via simulation, and hardware implementation, thus verifying its veracity. Hardware implementation is performed on the Quanser Qbot 2™ mobile robot, with a Vector-Nav VN-200™ IMU and Kinect™ camera from Microsoft. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=autonomous%20mobile%20robotics" title="autonomous mobile robotics">autonomous mobile robotics</a>, <a href="https://publications.waset.org/abstracts/search?q=dead%20reckoning" title=" dead reckoning"> dead reckoning</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20camera" title=" depth camera"> depth camera</a>, <a href="https://publications.waset.org/abstracts/search?q=inertial%20navigation" title=" inertial navigation"> inertial navigation</a>, <a href="https://publications.waset.org/abstracts/search?q=Kalman%20filtering" title=" Kalman filtering"> Kalman filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=localization" title=" localization"> localization</a>, <a href="https://publications.waset.org/abstracts/search?q=sensor%20fusion" title=" sensor fusion"> sensor fusion</a> </p> <a href="https://publications.waset.org/abstracts/134870/depth-camera-aided-dead-reckoning-localization-of-autonomous-mobile-robots-in-unstructured-gnss-denied-environments" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/134870.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">207</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">7844</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">7843</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">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">7842</span> Texture Identification Using Vision System: A Method to Predict Functionality of a Component</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Varsha%20Singh">Varsha Singh</a>, <a href="https://publications.waset.org/abstracts/search?q=Shraddha%20Prajapati"> Shraddha Prajapati</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20B.%20Kiran"> M. B. Kiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Texture identification is useful in predicting the functionality of a component. Many of the existing texture identification methods are of contact in nature, which limits its measuring speed. These contact measurement techniques use a diamond stylus and the diamond stylus being sharp going to damage the surface under inspection and hence these techniques can be used in statistical sampling. Though these contact methods are very accurate, they do not give complete information for full characterization of surface. In this context, the presented method assumes special significance. The method uses a relatively low cost vision system for image acquisition. Software is developed based on wavelet transform, for analyzing texture images. Specimens are made using different manufacturing process (shaping, grinding, milling etc.) During experimentation, the specimens are illuminated using proper lighting and texture images a capture using CCD camera connected to the vision system. The software installed in the vision system processes these images and subsequently identify the texture of manufacturing processes. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=diamond%20stylus" title="diamond stylus">diamond stylus</a>, <a href="https://publications.waset.org/abstracts/search?q=manufacturing%20process" title=" manufacturing process"> manufacturing process</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20identification" title=" texture identification"> texture identification</a>, <a href="https://publications.waset.org/abstracts/search?q=vision%20system" title=" vision system"> vision system</a> </p> <a href="https://publications.waset.org/abstracts/61722/texture-identification-using-vision-system-a-method-to-predict-functionality-of-a-component" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61722.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">7841</span> Multi-Sensor Image Fusion for Visible and Infrared Thermal Images</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amit%20Kumar%20Happy">Amit Kumar Happy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper is motivated by the importance of multi-sensor image fusion with a specific focus on infrared (IR) and visual image (VI) fusion for various applications, including military reconnaissance. Image fusion can be defined as the process of combining two or more source images into a single composite image with extended information content that improves visual perception or feature extraction. These images can be from different modalities like visible camera & IR thermal imager. While visible images are captured by reflected radiations in the visible spectrum, the thermal images are formed from thermal radiation (infrared) that may be reflected or self-emitted. A digital color camera captures the visible source image, and a thermal infrared camera acquires the thermal source image. In this paper, some image fusion algorithms based upon multi-scale transform (MST) and region-based selection rule with consistency verification have been proposed and presented. This research includes the implementation of the proposed image fusion algorithm in MATLAB along with a comparative analysis to decide the optimum number of levels for MST and the coefficient fusion rule. The results are presented, and several commonly used evaluation metrics are used to assess the suggested method's validity. Experiments show that the proposed approach is capable of producing good fusion results. While deploying our image fusion algorithm approaches, we observe several challenges from the popular image fusion methods. While high computational cost and complex processing steps of image fusion algorithms provide accurate fused results, they also make it hard to become deployed in systems and applications that require a real-time operation, high flexibility, and low computation ability. So, the methods presented in this paper offer good results with minimum time complexity. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20fusion" title="image fusion">image fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=IR%20thermal%20imager" title=" IR thermal imager"> IR thermal imager</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-sensor" title=" multi-sensor"> multi-sensor</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-scale%20transform" title=" multi-scale transform"> multi-scale transform</a> </p> <a href="https://publications.waset.org/abstracts/138086/multi-sensor-image-fusion-for-visible-and-infrared-thermal-images" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138086.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">115</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">7840</span> Solving Crimes through DNA Methylation Analysis</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ajay%20Kumar%20Rana">Ajay Kumar Rana</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Predicting human behaviour, discerning monozygotic twins or left over remnant tissues/fluids of a single human source remains a big challenge in forensic science. Recent advances in the field of DNA methylations which are broadly chemical hallmarks in response to environmental factors can certainly help to identify and discriminate various single-source DNA samples collected from the crime scenes. In this review, cytosine methylation of DNA has been methodologically discussed with its broad applications in many challenging forensic issues like body fluid identification, race/ethnicity identification, monozygotic twins dilemma, addiction or behavioural prediction, age prediction, or even authenticity of the human DNA. With the advent of next-generation sequencing techniques, blooming of DNA methylation datasets and together with standard molecular protocols, the prospect of investigating and solving the above issues and extracting the exact nature of the truth for reconstructing the crime scene events would be undoubtedly helpful in defending and solving the critical crime cases. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=DNA%20methylation" title="DNA methylation">DNA methylation</a>, <a href="https://publications.waset.org/abstracts/search?q=differentially%20methylated%20regions" title=" differentially methylated regions"> differentially methylated regions</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20identification" title=" human identification"> human identification</a>, <a href="https://publications.waset.org/abstracts/search?q=forensics" title=" forensics"> forensics</a> </p> <a href="https://publications.waset.org/abstracts/52307/solving-crimes-through-dna-methylation-analysis" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52307.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">321</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">7839</span> Real-Time Kinetic Analysis of Labor-Intensive Repetitive Tasks Using Depth-Sensing Camera</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sudip%20Subedi">Sudip Subedi</a>, <a href="https://publications.waset.org/abstracts/search?q=Nipesh%20Pradhananga"> Nipesh Pradhananga</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The musculoskeletal disorders, also known as MSDs, are common in construction workers. MSDs include lower back injuries, knee injuries, spinal injuries, and joint injuries, among others. Since most construction tasks are still manual, construction workers often need to perform repetitive, labor-intensive tasks. And they need to stay in the same or an awkward posture for an extended time while performing such tasks. It induces significant stress to the joints and spines, increasing the risk of getting into MSDs. Manual monitoring of such tasks is virtually impossible with the handful of safety managers in a construction site. This paper proposes a methodology for performing kinetic analysis of the working postures while performing such tasks in real-time. Skeletal of different workers will be tracked using a depth-sensing camera while performing the task to create training data for identifying the best posture. For this, the kinetic analysis will be performed using a human musculoskeletal model in an open-source software system (OpenSim) to visualize the stress induced by essential joints. The “safe posture” inducing lowest stress on essential joints will be computed for different actions involved in the task. The identified “safe posture” will serve as a basis for real-time monitoring and identification of awkward and unsafe postural behaviors of construction workers. Besides, the temporal simulation will be carried out to find the associated long-term effect of repetitive exposure to such observed postures. This will help to create awareness in workers about potential future health hazards and encourage them to work safely. Furthermore, the collected individual data can then be used to provide need-based personalized training to the construction workers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=construction%20workers%E2%80%99%20safety" title="construction workers’ safety">construction workers’ safety</a>, <a href="https://publications.waset.org/abstracts/search?q=depth%20sensing%20camera" title=" depth sensing camera"> depth sensing camera</a>, <a href="https://publications.waset.org/abstracts/search?q=human%20body%20kinetics" title=" human body kinetics"> human body kinetics</a>, <a href="https://publications.waset.org/abstracts/search?q=musculoskeletal%20disorders" title=" musculoskeletal disorders"> musculoskeletal disorders</a>, <a href="https://publications.waset.org/abstracts/search?q=real%20time%20monitoring" title=" real time monitoring"> real time monitoring</a>, <a href="https://publications.waset.org/abstracts/search?q=repetitive%20labor-intensive%20tasks" title=" repetitive labor-intensive tasks"> repetitive labor-intensive tasks</a> </p> <a href="https://publications.waset.org/abstracts/111914/real-time-kinetic-analysis-of-labor-intensive-repetitive-tasks-using-depth-sensing-camera" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/111914.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">130</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">7838</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">7837</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">7836</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">7835</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">7834</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 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