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time</title> <meta name="description" content="Search results for: video time"> <meta name="keywords" content="video time"> <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 ml-auto" 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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="video time"> <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> 18830</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: video time</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18830</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">18829</span> Lecture Video Indexing and Retrieval Using Topic Keywords</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20J.%20Sandesh">B. J. Sandesh</a>, <a href="https://publications.waset.org/abstracts/search?q=Saurabha%20Jirgi"> Saurabha Jirgi</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Vidya"> S. Vidya</a>, <a href="https://publications.waset.org/abstracts/search?q=Prakash%20Eljer"> Prakash Eljer</a>, <a href="https://publications.waset.org/abstracts/search?q=Gowri%20Srinivasa"> Gowri Srinivasa</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we propose a framework to help users to search and retrieve the portions in the lecture video of their interest. This is achieved by temporally segmenting and indexing the lecture video using the topic keywords. We use transcribed text from the video and documents relevant to the video topic extracted from the web for this purpose. The keywords for indexing are found by applying the non-negative matrix factorization (NMF) topic modeling techniques on the web documents. Our proposed technique first creates indices on the transcribed documents using the topic keywords, and these are mapped to the video to find the start and end time of the portions of the video for a particular topic. This time information is stored in the index table along with the topic keyword which is used to retrieve the specific portions of the video for the query provided by the users. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20indexing%20and%20retrieval" title="video indexing and retrieval">video indexing and retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=lecture%20videos" title=" lecture videos"> lecture videos</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20based%20video%20search" title=" content based video search"> content based video search</a>, <a href="https://publications.waset.org/abstracts/search?q=multimodal%20indexing" title=" multimodal indexing"> multimodal indexing</a> </p> <a href="https://publications.waset.org/abstracts/77066/lecture-video-indexing-and-retrieval-using-topic-keywords" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77066.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">250</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">18828</span> Extraction of Text Subtitles in Multimedia Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Amarjit%20Singh">Amarjit Singh</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a method for extraction of text subtitles in large video is proposed. The video data needs to be annotated for many multimedia applications. Text is incorporated in digital video for the motive of providing useful information about that video. So need arises to detect text present in video to understanding and video indexing. This is achieved in two steps. First step is text localization and the second step is text verification. The method of text detection can be extended to text recognition which finds applications in automatic video indexing; video annotation and content based video retrieval. The method has been tested on various types of videos. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video" title="video">video</a>, <a href="https://publications.waset.org/abstracts/search?q=subtitles" title=" subtitles"> subtitles</a>, <a href="https://publications.waset.org/abstracts/search?q=extraction" title=" extraction"> extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=annotation" title=" annotation"> annotation</a>, <a href="https://publications.waset.org/abstracts/search?q=frames" title=" frames"> frames</a> </p> <a href="https://publications.waset.org/abstracts/24441/extraction-of-text-subtitles-in-multimedia-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/24441.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">601</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">18827</span> Video Summarization: Techniques and Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Zaynab%20El%20Khattabi">Zaynab El Khattabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Youness%20Tabii"> Youness Tabii</a>, <a href="https://publications.waset.org/abstracts/search?q=Abdelhamid%20Benkaddour"> Abdelhamid Benkaddour</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, huge amount of multimedia repositories make the browsing, retrieval and delivery of video contents very slow and even difficult tasks. Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. In this paper, we focus on approaches to video summarization. The video summaries can be generated in many different forms. However, two fundamentals ways to generate summaries are static and dynamic. We present different techniques for each mode in the literature and describe some features used for generating video summaries. We conclude with perspective for further research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=static%20summarization" title=" static summarization"> static summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20skimming" title=" video skimming"> video skimming</a>, <a href="https://publications.waset.org/abstracts/search?q=semantic%20features" title=" semantic features"> semantic features</a> </p> <a href="https://publications.waset.org/abstracts/27644/video-summarization-techniques-and-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/27644.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">401</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">18826</span> Multimodal Convolutional Neural Network for Musical Instrument Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Yagya%20Raj%20Pandeya">Yagya Raj Pandeya</a>, <a href="https://publications.waset.org/abstracts/search?q=Joonwhoan%20Lee"> Joonwhoan Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimodal" title="multimodal">multimodal</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20convolution" title=" 3D convolution"> 3D convolution</a>, <a href="https://publications.waset.org/abstracts/search?q=music-video%20feature%20extraction" title=" music-video feature extraction"> music-video feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=generalized%20mean" title=" generalized mean"> generalized mean</a> </p> <a href="https://publications.waset.org/abstracts/104041/multimodal-convolutional-neural-network-for-musical-instrument-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104041.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">215</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">18825</span> Distributed Processing for Content Based Lecture Video Retrieval on Hadoop Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=U.%20S.%20N.%20Raju">U. S. N. Raju</a>, <a href="https://publications.waset.org/abstracts/search?q=Kothuri%20Sai%20Kiran"> Kothuri Sai Kiran</a>, <a href="https://publications.waset.org/abstracts/search?q=Meena%20G.%20Kamal"> Meena G. Kamal</a>, <a href="https://publications.waset.org/abstracts/search?q=Vinay%20Nikhil%20Pabba"> Vinay Nikhil Pabba</a>, <a href="https://publications.waset.org/abstracts/search?q=Suresh%20Kanaparthi"> Suresh Kanaparthi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efﬁcient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20lectures" title="video lectures">video lectures</a>, <a href="https://publications.waset.org/abstracts/search?q=big%20video%20data" title=" big video data"> big video data</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20retrieval" title=" video retrieval"> video retrieval</a>, <a href="https://publications.waset.org/abstracts/search?q=hadoop" title=" hadoop"> hadoop</a> </p> <a href="https://publications.waset.org/abstracts/26648/distributed-processing-for-content-based-lecture-video-retrieval-on-hadoop-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/26648.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">534</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">18824</span> Video Stabilization Using Feature Point Matching</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shamsundar%20Kulkarni">Shamsundar Kulkarni</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video capturing by non-professionals will lead to unanticipated effects. Such as image distortion, image blurring etc. Hence, many researchers study such drawbacks to enhance the quality of videos. In this paper, an algorithm is proposed to stabilize jittery videos .A stable output video will be attained without the effect of jitter which is caused due to shaking of handheld camera during video recording. Firstly, salient points from each frame from the input video are identified and processed followed by optimizing and stabilize the video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20stabilization" title="video stabilization">video stabilization</a>, <a href="https://publications.waset.org/abstracts/search?q=point%20feature%20matching" title=" point feature matching"> point feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=salient%20points" title=" salient points"> salient points</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20quality%20measurement" title=" image quality measurement"> image quality measurement</a> </p> <a href="https://publications.waset.org/abstracts/57341/video-stabilization-using-feature-point-matching" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57341.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">313</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">18823</span> Structural Analysis on the Composition of Video Game Virtual Spaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qin%20Luofeng">Qin Luofeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Shen%20Siqi"> Shen Siqi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> For the 58 years since the first video game came into being, the video game industry is getting through an explosive evolution from then on. Video games exert great influence on society and become a reflection of public life to some extent. Video game virtual spaces are where activities are taking place like real spaces. And that’s the reason why some architects pay attention to video games. However, compared to the researches on the appearance of games, we observe a lack of theoretical comprehensive on the construction of video game virtual spaces. The research method of this paper is to collect literature and conduct theoretical research about the virtual space in video games firstly. And then analogizing the opinions on the space phenomena from the theory of literature and films. Finally, this paper proposes a three-layer framework for the construction of video game virtual spaces: “algorithmic space-narrative space players space”, which correspond to the exterior, expressive, affective parts of the game space. Also, we illustrate each sub-space according to numerous instances of published video games. Hoping this writing could promote the interactive development of video games and architecture. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20game" title="video game">video game</a>, <a href="https://publications.waset.org/abstracts/search?q=virtual%20space" title=" virtual space"> virtual space</a>, <a href="https://publications.waset.org/abstracts/search?q=narrativity" title=" narrativity"> narrativity</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20space" title=" social space"> social space</a>, <a href="https://publications.waset.org/abstracts/search?q=emotional%20connection" title=" emotional connection"> emotional connection</a> </p> <a href="https://publications.waset.org/abstracts/118519/structural-analysis-on-the-composition-of-video-game-virtual-spaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118519.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">267</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">18822</span> Key Frame Based Video Summarization via Dependency Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Janya%20Sainui">Janya Sainui</a> </p> <p class="card-text"><strong>Abstract:</strong></p> As a rapid growth of digital videos and data communications, video summarization that provides a shorter version of the video for fast video browsing and retrieval is necessary. Key frame extraction is one of the mechanisms to generate video summary. In general, the extracted key frames should both represent the entire video content and contain minimum redundancy. However, most of the existing approaches heuristically select key frames; hence, the selected key frames may not be the most different frames and/or not cover the entire content of a video. In this paper, we propose a method of video summarization which provides the reasonable objective functions for selecting key frames. In particular, we apply a statistical dependency measure called quadratic mutual informaion as our objective functions for maximizing the coverage of the entire video content as well as minimizing the redundancy among selected key frames. The proposed key frame extraction algorithm finds key frames as an optimization problem. Through experiments, we demonstrate the success of the proposed video summarization approach that produces video summary with better coverage of the entire video content while less redundancy among key frames comparing to the state-of-the-art approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title="video summarization">video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20frame%20extraction" title=" key frame extraction"> key frame extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=dependency%20measure" title=" dependency measure"> dependency measure</a>, <a href="https://publications.waset.org/abstracts/search?q=quadratic%20mutual%20information" title=" quadratic mutual information"> quadratic mutual information</a> </p> <a href="https://publications.waset.org/abstracts/75218/key-frame-based-video-summarization-via-dependency-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/75218.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">266</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">18821</span> FLIME - Fast Low Light Image Enhancement for Real-Time Video</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Vinay%20P.">Vinay P.</a>, <a href="https://publications.waset.org/abstracts/search?q=Srinivas%20K.%20S."> Srinivas K. S.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Low Light Image Enhancement is of utmost impor- tance in computer vision based tasks. Applications include vision systems for autonomous driving, night vision devices for defence systems, low light object detection tasks. Many of the existing deep learning methods are resource intensive during the inference step and take considerable time for processing. The algorithm should take considerably less than 41 milliseconds in order to process a real-time video feed with 24 frames per second and should be even less for a video with 30 or 60 frames per second. The paper presents a fast and efficient solution which has two main advantages, it has the potential to be used for a real-time video feed, and it can be used in low compute environments because of the lightweight nature. The proposed solution is a pipeline of three steps, the first one is the use of a simple function to map input RGB values to output RGB values, the second is to balance the colors and the final step is to adjust the contrast of the image. Hence a custom dataset is carefully prepared using images taken in low and bright lighting conditions. The preparation of the dataset, the proposed model, the processing time are discussed in detail and the quality of the enhanced images using different methods is shown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=low%20light%20image%20enhancement" title="low light image enhancement">low light image enhancement</a>, <a href="https://publications.waset.org/abstracts/search?q=real-time%20video" title=" real-time video"> real-time video</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20vision" title=" computer vision"> computer vision</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a> </p> <a href="https://publications.waset.org/abstracts/144526/flime-fast-low-light-image-enhancement-for-real-time-video" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/144526.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">206</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">18820</span> Efficient Storage and Intelligent Retrieval of Multimedia Streams Using H. 265</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=S.%20Sarumathi">S. Sarumathi</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Deepadharani"> C. Deepadharani</a>, <a href="https://publications.waset.org/abstracts/search?q=Garimella%20Archana"> Garimella Archana</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Dakshayani"> S. Dakshayani</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Logeshwaran"> D. Logeshwaran</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Jayakumar"> D. Jayakumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Vijayarangan%20Natarajan"> Vijayarangan Natarajan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The need of the hour for the customers who use a dial-up or a low broadband connection for their internet services is to access HD video data. This can be achieved by developing a new video format using H. 265. This is the latest video codec standard developed by ISO/IEC Moving Picture Experts Group (MPEG) and ITU-T Video Coding Experts Group (VCEG) on April 2013. This new standard for video compression has the potential to deliver higher performance than the earlier standards such as H. 264/AVC. In comparison with H. 264, HEVC offers a clearer, higher quality image at half the original bitrate. At this lower bitrate, it is possible to transmit high definition videos using low bandwidth. It doubles the data compression ratio supporting 8K Ultra HD and resolutions up to 8192×4320. In the proposed model, we design a new video format which supports this H. 265 standard. The major areas of applications in the coming future would lead to enhancements in the performance level of digital television like Tata Sky and Sun Direct, BluRay Discs, Mobile Video, Video Conferencing and Internet and Live Video streaming. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=access%20HD%20video" title="access HD video">access HD video</a>, <a href="https://publications.waset.org/abstracts/search?q=H.%20265%20video%20standard" title=" H. 265 video standard"> H. 265 video standard</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20performance" title=" high performance"> high performance</a>, <a href="https://publications.waset.org/abstracts/search?q=high%20quality%20image" title=" high quality image"> high quality image</a>, <a href="https://publications.waset.org/abstracts/search?q=low%20bandwidth" title=" low bandwidth"> low bandwidth</a>, <a href="https://publications.waset.org/abstracts/search?q=new%20video%20format" title=" new video format"> new video format</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20streaming%20applications" title=" video streaming applications"> video streaming applications</a> </p> <a href="https://publications.waset.org/abstracts/1881/efficient-storage-and-intelligent-retrieval-of-multimedia-streams-using-h-265" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/1881.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">354</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">18819</span> Adaptive Multiple Transforms Hardware Architecture for Versatile Video Coding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=T.%20Damak">T. Damak</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Houidi"> S. Houidi</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Ben%20Ayed"> M. A. Ben Ayed</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Masmoudi"> N. Masmoudi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Versatile Video Coding standard (VVC) is actually under development by the Joint Video Exploration Team (or JVET). An Adaptive Multiple Transforms (AMT) approach was announced. It is based on different transform modules that provided an efficient coding. However, the AMT solution raises several issues especially regarding the complexity of the selected set of transforms. This can be an important issue, particularly for a future industrial adoption. This paper proposed an efficient hardware implementation of the most used transform in AMT approach: the DCT II. The developed circuit is adapted to different block sizes and can reach a minimum frequency of 192 MHz allowing an optimized execution time. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20multiple%20transforms" title="adaptive multiple transforms">adaptive multiple transforms</a>, <a href="https://publications.waset.org/abstracts/search?q=AMT" title=" AMT"> AMT</a>, <a href="https://publications.waset.org/abstracts/search?q=DCT%20II" title=" DCT II"> DCT II</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware" title=" hardware"> hardware</a>, <a href="https://publications.waset.org/abstracts/search?q=transform" title=" transform"> transform</a>, <a href="https://publications.waset.org/abstracts/search?q=versatile%20video%20coding" title=" versatile video coding"> versatile video coding</a>, <a href="https://publications.waset.org/abstracts/search?q=VVC" title=" VVC"> VVC</a> </p> <a href="https://publications.waset.org/abstracts/108705/adaptive-multiple-transforms-hardware-architecture-for-versatile-video-coding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108705.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">146</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">18818</span> Tackling the Digital Divide: Enhancing Video Consultation Access for Digital Illiterate Patients in the Hospital</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wieke%20Ellen%20Bouwes">Wieke Ellen Bouwes</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study aims to unravel which factors enhance accessibility of video consultations (VCs) for patients with low digital literacy. Thirteen in-depth interviews with patients, hospital employees, eHealth experts, and digital support organizations were held. Patients with low digital literacy received in-home support during real-time video consultations and are observed during the set-up of these consultations. Key findings highlight the importance of patient acceptance, emphasizing video consultations benefits and avoiding standardized courses. The lack of a uniform video consultation system across healthcare providers poses a barrier. Familiarity with support organizations – to support patients in usage of digital tools - among healthcare practitioners enhances accessibility. Moreover, considerations regarding the Dutch General Data Protection Regulation (GDPR) law influence support patients receive. Also, provider readiness to use video consultations influences patient access. Further, alignment between learning styles and support methods seems to determine abilities to learn how to use video consultations. Future research could delve into tailored learning styles and technological solutions for remote access to further explore effectiveness of learning methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=video%20consultations" title="video consultations">video consultations</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20literacy%20skills" title=" digital literacy skills"> digital literacy skills</a>, <a href="https://publications.waset.org/abstracts/search?q=effectiveness%20of%20support" title=" effectiveness of support"> effectiveness of support</a>, <a href="https://publications.waset.org/abstracts/search?q=intra-%20and%20inter-organizational%20relationships" title=" intra- and inter-organizational relationships"> intra- and inter-organizational relationships</a>, <a href="https://publications.waset.org/abstracts/search?q=patient%20acceptance%20of%20video%20consultations" title=" patient acceptance of video consultations"> patient acceptance of video consultations</a> </p> <a href="https://publications.waset.org/abstracts/173756/tackling-the-digital-divide-enhancing-video-consultation-access-for-digital-illiterate-patients-in-the-hospital" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/173756.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">74</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18817</span> Video Shot Detection and Key Frame Extraction Using Faber-Shauder DWT and SVD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Assma%20Azeroual">Assma Azeroual</a>, <a href="https://publications.waset.org/abstracts/search?q=Karim%20Afdel"> Karim Afdel</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohamed%20El%20Hajji"> Mohamed El Hajji</a>, <a href="https://publications.waset.org/abstracts/search?q=Hassan%20Douzi"> Hassan Douzi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Key frame extraction methods select the most representative frames of a video, which can be used in different areas of video processing such as video retrieval, video summary, and video indexing. In this paper we present a novel approach for extracting key frames from video sequences. The frame is characterized uniquely by his contours which are represented by the dominant blocks. These dominant blocks are located on the contours and its near textures. When the video frames have a noticeable changement, its dominant blocks changed, then we can extracte a key frame. The dominant blocks of every frame is computed, and then feature vectors are extracted from the dominant blocks image of each frame and arranged in a feature matrix. Singular Value Decomposition is used to calculate sliding windows ranks of those matrices. Finally the computed ranks are traced and then we are able to extract key frames of a video. Experimental results show that the proposed approach is robust against a large range of digital effects used during shot transition. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=FSDWT" title="FSDWT">FSDWT</a>, <a href="https://publications.waset.org/abstracts/search?q=key%20frame%20extraction" title=" key frame extraction"> key frame extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=shot%20detection" title=" shot detection"> shot detection</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a> </p> <a href="https://publications.waset.org/abstracts/18296/video-shot-detection-and-key-frame-extraction-using-faber-shauder-dwt-and-svd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18296.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">398</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">18816</span> Assisted Video Colorization Using Texture Descriptors</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Andre%20Peres%20Ramos">Andre Peres Ramos</a>, <a href="https://publications.waset.org/abstracts/search?q=Franklin%20Cesar%20Flores"> Franklin Cesar Flores</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Colorization is the process of add colors to a monochromatic image or video. Usually, the process involves to segment the image in regions of interest and then apply colors to each one, for videos, this process is repeated for each frame, which makes it a tedious and time-consuming job. We propose a new assisted method for video colorization; the user only has to colorize one frame, and then the colors are propagated to following frames. The user can intervene at any time to correct eventual errors in color assignment. The method consists of to extract intensity and texture descriptors from the frames and then perform a feature matching to determine the best color for each segment. To reduce computation time and give a better spatial coherence we narrow the area of search and give weights for each feature to emphasize texture descriptors. To give a more natural result, we use an optimization algorithm to make the color propagation. Experimental results in several image sequences, compared to others existing methods, demonstrates that the proposed method perform a better colorization with less time and user interference. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=colorization" title="colorization">colorization</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20matching" title=" feature matching"> feature matching</a>, <a href="https://publications.waset.org/abstracts/search?q=texture%20descriptors" title=" texture descriptors"> texture descriptors</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20segmentation" title=" video segmentation"> video segmentation</a> </p> <a href="https://publications.waset.org/abstracts/97191/assisted-video-colorization-using-texture-descriptors" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/97191.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">162</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">18815</span> Video Foreground Detection Based on Adaptive Mixture Gaussian Model for Video Surveillance Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20A.%20Alavianmehr">M. A. Alavianmehr</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Tashk"> A. Tashk</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Sodagaran"> A. Sodagaran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Modeling background and moving objects are significant techniques for video surveillance and other video processing applications. This paper presents a foreground detection algorithm that is robust against illumination changes and noise based on adaptive mixture Gaussian model (GMM), and provides a novel and practical choice for intelligent video surveillance systems using static cameras. In the previous methods, the image of still objects (background image) is not significant. On the contrary, this method is based on forming a meticulous background image and exploiting it for separating moving objects from their background. The background image is specified either manually, by taking an image without vehicles, or is detected in real-time by forming a mathematical or exponential average of successive images. The proposed scheme can offer low image degradation. The simulation results demonstrate high degree of performance for the proposed method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title="image processing">image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=background%20models" title=" background models"> background models</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a>, <a href="https://publications.waset.org/abstracts/search?q=foreground%20detection" title=" foreground detection"> foreground detection</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20mixture%20model" title=" Gaussian mixture model"> Gaussian mixture model</a> </p> <a href="https://publications.waset.org/abstracts/16364/video-foreground-detection-based-on-adaptive-mixture-gaussian-model-for-video-surveillance-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16364.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">516</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">18814</span> Surveillance Video Summarization Based on Histogram Differencing and Sum Conditional Variance</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nada%20Jasim%20Habeeb">Nada Jasim Habeeb</a>, <a href="https://publications.waset.org/abstracts/search?q=Rana%20Saad%20Mohammed"> Rana Saad Mohammed</a>, <a href="https://publications.waset.org/abstracts/search?q=Muntaha%20Khudair%20Abbass"> Muntaha Khudair Abbass </a> </p> <p class="card-text"><strong>Abstract:</strong></p> For more efficient and fast video summarization, this paper presents a surveillance video summarization method. The presented method works to improve video summarization technique. This method depends on temporal differencing to extract most important data from large video stream. This method uses histogram differencing and Sum Conditional Variance which is robust against to illumination variations in order to extract motion objects. The experimental results showed that the presented method gives better output compared with temporal differencing based summarization techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=temporal%20differencing" title="temporal differencing">temporal differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20summarization" title=" video summarization"> video summarization</a>, <a href="https://publications.waset.org/abstracts/search?q=histogram%20differencing" title=" histogram differencing"> histogram differencing</a>, <a href="https://publications.waset.org/abstracts/search?q=sum%20conditional%20variance" title=" sum conditional variance"> sum conditional variance</a> </p> <a href="https://publications.waset.org/abstracts/54404/surveillance-video-summarization-based-on-histogram-differencing-and-sum-conditional-variance" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/54404.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">18813</span> Method Comprising One to One Web Based Real Time Communications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Lata%20Kiran%20Dey">Lata Kiran Dey</a>, <a href="https://publications.waset.org/abstracts/search?q=Rajendra%20Kumar"> Rajendra Kumar</a>, <a href="https://publications.waset.org/abstracts/search?q=Biren%20Karmakar"> Biren Karmakar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Web Real Time Communications is a collection of standards, protocols, which provides real-time communications capabilities between web browsers and devices. This paper outlines the design and further implementation of web real-time communications on secure web applications having audio and video call capabilities. This proposed application may put up a system that will be able to work over both desktops as well as the mobile browser. Though, WebRTC also gives a set of JavaScript standard RTC APIs, which primarily works over the real-time communication framework. This helps to build a suitable communication application, which enables the audio, video, and message transfer in between the today’s modern browsers having WebRTC support. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=WebRTC" title="WebRTC">WebRTC</a>, <a href="https://publications.waset.org/abstracts/search?q=SIP" title=" SIP"> SIP</a>, <a href="https://publications.waset.org/abstracts/search?q=RTC" title=" RTC"> RTC</a>, <a href="https://publications.waset.org/abstracts/search?q=JavaScript" title=" JavaScript"> JavaScript</a>, <a href="https://publications.waset.org/abstracts/search?q=SRTP" title=" SRTP"> SRTP</a>, <a href="https://publications.waset.org/abstracts/search?q=secure%20web%20sockets" title=" secure web sockets"> secure web sockets</a>, <a href="https://publications.waset.org/abstracts/search?q=browser" title=" browser"> browser</a> </p> <a href="https://publications.waset.org/abstracts/151216/method-comprising-one-to-one-web-based-real-time-communications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/151216.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">18812</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">18811</span> Performance Evaluation of Routing Protocols for Video Conference over MPLS VPN Network</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abdullah%20Al%20Mamun">Abdullah Al Mamun</a>, <a href="https://publications.waset.org/abstracts/search?q=Tarek%20R.%20Sheltami"> Tarek R. Sheltami</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Video conferencing is a highly demanding facility now a days in order to its real time characteristics, but faster communication is the prior requirement of this technology. Multi Protocol Label Switching (MPLS) IP Virtual Private Network (VPN) address this problem and it is able to make a communication faster than others techniques. However, this paper studies the performance comparison of video traffic between two routing protocols namely the Enhanced Interior Gateway Protocol(EIGRP) and Open Shortest Path First (OSPF). The combination of traditional routing and MPLS improve the forwarding mechanism, scalability and overall network performance. We will use GNS3 and OPNET Modeler 14.5 to simulate many different scenarios and metrics such as delay, jitter and mean opinion score (MOS) value are measured. The simulation result will show that OSPF and BGP-MPLS VPN offers best performance for video conferencing application. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=OSPF" title="OSPF">OSPF</a>, <a href="https://publications.waset.org/abstracts/search?q=BGP" title=" BGP"> BGP</a>, <a href="https://publications.waset.org/abstracts/search?q=EIGRP" title=" EIGRP"> EIGRP</a>, <a href="https://publications.waset.org/abstracts/search?q=MPLS" title=" MPLS"> MPLS</a>, <a href="https://publications.waset.org/abstracts/search?q=Video%20conference" title=" Video conference"> Video conference</a>, <a href="https://publications.waset.org/abstracts/search?q=Provider%20router" title=" Provider router"> Provider router</a>, <a href="https://publications.waset.org/abstracts/search?q=edge%20router" title=" edge router"> edge router</a>, <a href="https://publications.waset.org/abstracts/search?q=layer3%20VPN" title=" layer3 VPN"> layer3 VPN</a> </p> <a href="https://publications.waset.org/abstracts/32093/performance-evaluation-of-routing-protocols-for-video-conference-over-mpls-vpn-network" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/32093.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">331</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">18810</span> Evaluating the Performance of Existing Full-Reference Quality Metrics on High Dynamic Range (HDR) Video Content</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Maryam%20Azimi">Maryam Azimi</a>, <a href="https://publications.waset.org/abstracts/search?q=Amin%20Banitalebi-Dehkordi"> Amin Banitalebi-Dehkordi</a>, <a href="https://publications.waset.org/abstracts/search?q=Yuanyuan%20Dong"> Yuanyuan Dong</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahsa%20T.%20Pourazad"> Mahsa T. Pourazad</a>, <a href="https://publications.waset.org/abstracts/search?q=Panos%20Nasiopoulos"> Panos Nasiopoulos</a> </p> <p class="card-text"><strong>Abstract:</strong></p> While there exists a wide variety of Low Dynamic Range (LDR) quality metrics, only a limited number of metrics are designed specifically for the High Dynamic Range (HDR) content. With the introduction of HDR video compression standardization effort by international standardization bodies, the need for an efficient video quality metric for HDR applications has become more pronounced. The objective of this study is to compare the performance of the existing full-reference LDR and HDR video quality metrics on HDR content and identify the most effective one for HDR applications. To this end, a new HDR video data set is created, which consists of representative indoor and outdoor video sequences with different brightness, motion levels and different representing types of distortions. The quality of each distorted video in this data set is evaluated both subjectively and objectively. The correlation between the subjective and objective results confirm that VIF quality metric outperforms all to their tested metrics in the presence of the tested types of distortions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=HDR" title="HDR">HDR</a>, <a href="https://publications.waset.org/abstracts/search?q=dynamic%20range" title=" dynamic range"> dynamic range</a>, <a href="https://publications.waset.org/abstracts/search?q=LDR" title=" LDR"> LDR</a>, <a href="https://publications.waset.org/abstracts/search?q=subjective%20evaluation" title=" subjective evaluation"> subjective evaluation</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20compression" title=" video compression"> video compression</a>, <a href="https://publications.waset.org/abstracts/search?q=HEVC" title=" HEVC"> HEVC</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20quality%20metrics" title=" video quality metrics"> video quality metrics</a> </p> <a href="https://publications.waset.org/abstracts/18171/evaluating-the-performance-of-existing-full-reference-quality-metrics-on-high-dynamic-range-hdr-video-content" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/18171.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">525</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">18809</span> Extending Image Captioning to Video Captioning Using Encoder-Decoder</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sikiru%20Ademola%20Adewale">Sikiru Ademola Adewale</a>, <a href="https://publications.waset.org/abstracts/search?q=Joe%20Thomas"> Joe Thomas</a>, <a href="https://publications.waset.org/abstracts/search?q=Bolanle%20Hafiz%20Matti"> Bolanle Hafiz Matti</a>, <a href="https://publications.waset.org/abstracts/search?q=Tosin%20Ige"> Tosin Ige</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This project demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=decoder" title="decoder">decoder</a>, <a href="https://publications.waset.org/abstracts/search?q=encoder" title=" encoder"> encoder</a>, <a href="https://publications.waset.org/abstracts/search?q=many-to-many%20mapping" title=" many-to-many mapping"> many-to-many mapping</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20captioning" title=" video captioning"> video captioning</a>, <a href="https://publications.waset.org/abstracts/search?q=2-gram%20BLEU" title=" 2-gram BLEU"> 2-gram BLEU</a> </p> <a href="https://publications.waset.org/abstracts/164540/extending-image-captioning-to-video-captioning-using-encoder-decoder" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/164540.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">108</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">18808</span> H.264 Video Privacy Protection Method Using Regions of Interest Encryption</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taekyun%20Doo">Taekyun Doo</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheongmin%20Ji"> Cheongmin Ji</a>, <a href="https://publications.waset.org/abstracts/search?q=Manpyo%20Hong"> Manpyo Hong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Like a closed-circuit television (CCTV), video surveillance system is widely placed for gathering video from unspecified people to prevent crime, surveillance, or many other purposes. However, abuse of CCTV brings about concerns of personal privacy invasions. In this paper, we propose an encryption method to protect personal privacy system in H.264 compressed video bitstream with encrypting only regions of interest (ROI). There is no need to change the existing video surveillance system. In addition, encrypting ROI in compressed video bitstream is a challenging work due to spatial and temporal drift errors. For this reason, we propose a novel drift mitigation method when ROI is encrypted. The proposed method was implemented by using JM reference software based on the H.264 compressed videos, and experimental results show the verification of our proposed methods and its effectiveness. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.264%2FAVC" title="H.264/AVC">H.264/AVC</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20encryption" title=" video encryption"> video encryption</a>, <a href="https://publications.waset.org/abstracts/search?q=privacy%20protection" title=" privacy protection"> privacy protection</a>, <a href="https://publications.waset.org/abstracts/search?q=post%20compression" title=" post compression"> post compression</a>, <a href="https://publications.waset.org/abstracts/search?q=region%20of%20interest" title=" region of interest"> region of interest</a> </p> <a href="https://publications.waset.org/abstracts/57651/h264-video-privacy-protection-method-using-regions-of-interest-encryption" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57651.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">340</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">18807</span> The Impact of Keyword and Full Video Captioning on Listening Comprehension</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elias%20Bensalem">Elias Bensalem</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the effect of two types of captioning (full and keyword captioning) on listening comprehension. Thirty-six university-level EFL students participated in the study. They were randomly assigned to watch three video clips under three conditions. The first group watched the video clips with full captions. The second group watched the same video clips with keyword captions. The control group watched the video clips without captions. After watching each clip, participants took a listening comprehension test. At the end of the experiment, participants completed a questionnaire to measure their perceptions about the use of captions and the video clips they watched. Results indicated that the full captioning group significantly outperformed both the keyword captioning and the no captioning group on the listening comprehension tests. However, this study did not find any significant difference between the keyword captioning group and the no captioning group. Results of the survey suggest that keyword captioning were a source of distraction for participants. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=captions" title="captions">captions</a>, <a href="https://publications.waset.org/abstracts/search?q=EFL" title=" EFL"> EFL</a>, <a href="https://publications.waset.org/abstracts/search?q=listening%20comprehension" title=" listening comprehension"> listening comprehension</a>, <a href="https://publications.waset.org/abstracts/search?q=video" title=" video"> video</a> </p> <a href="https://publications.waset.org/abstracts/62467/the-impact-of-keyword-and-full-video-captioning-on-listening-comprehension" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/62467.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">262</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">18806</span> A Study on the Relationship Between Adult Videogaming and Wellbeing, Health, and Labor Supply</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=William%20Marquis">William Marquis</a>, <a href="https://publications.waset.org/abstracts/search?q=Fang%20Dong"> Fang Dong</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There has been a growing concern in recent years over the economic and social effects of adult video gaming. It has been estimated that the number of people who played video games during the COVID-19 pandemic is close to three billion, and there is evidence that this form of entertainment is here to stay. Many people are concerned that this growing use of time could crowd out time that could be spent on alternative forms of entertainment with family, friends, sports, and other social activities that build community. For example, recent studies of children suggest that playing videogames crowds out time that could be spent on homework, watching TV, or in other social activities. Similar studies of adults have shown that video gaming is negatively associated with earnings, time spent at work, and socializing with others. The primary objective of this paper is to examine how time adults spend on video gaming could displace time they could spend working and on activities that enhance their health and well-being. We use data from the American Time Use Survey (ATUS), maintained by the Bureau of Labor Statistics, to analyze the effects of time-use decisions on three measures of well-being. We pool the ATUS Well-being Module for multiple years, 2010, 2012, 2013, and 2021, along with the ATUS Activity and Who files for these years. This pooled data set provides three broad measures of well-being, e.g., health, life satisfaction, and emotional well-being. Seven variants of each are used as a dependent variable in different multivariate regressions. We add to the existing literature in the following ways. First, we investigate whether the time adults spend in video gaming crowds out time spent working or in social activities that promote health and life satisfaction. Second, we investigate the relationship between adult gaming and their emotional well-being, also known as negative or positive affect, a factor that is related to depression, health, and labor market productivity. The results of this study suggest that the time adult gamers spend on video gaming has no effect on their supply of labor, a negligible effect on their time spent socializing and studying, and mixed effects on their emotional well-being, such as increasing feelings of pain and reducing feelings of happiness and stress. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=online%20gaming" title="online gaming">online gaming</a>, <a href="https://publications.waset.org/abstracts/search?q=health" title=" health"> health</a>, <a href="https://publications.waset.org/abstracts/search?q=social%20capital" title=" social capital"> social capital</a>, <a href="https://publications.waset.org/abstracts/search?q=emotional%20wellbeing" title=" emotional wellbeing"> emotional wellbeing</a> </p> <a href="https://publications.waset.org/abstracts/183822/a-study-on-the-relationship-between-adult-videogaming-and-wellbeing-health-and-labor-supply" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183822.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">45</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">18805</span> Temporally Coherent 3D Animation Reconstruction from RGB-D Video Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Salam%20Khalifa">Salam Khalifa</a>, <a href="https://publications.waset.org/abstracts/search?q=Naveed%20Ahmed"> Naveed Ahmed</a> </p> <p class="card-text"><strong>Abstract:</strong></p> We present a new method to reconstruct a temporally coherent 3D animation from single or multi-view RGB-D video data using unbiased feature point sampling. Given RGB-D video data, in form of a 3D point cloud sequence, our method first extracts feature points using both color and depth information. In the subsequent steps, these feature points are used to match two 3D point clouds in consecutive frames independent of their resolution. Our new motion vectors based dynamic alignment method then fully reconstruct a spatio-temporally coherent 3D animation. We perform extensive quantitative validation using novel error functions to analyze the results. We show that despite the limiting factors of temporal and spatial noise associated to RGB-D data, it is possible to extract temporal coherence to faithfully reconstruct a temporally coherent 3D animation from RGB-D video data. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=3D%20video" title="3D video">3D video</a>, <a href="https://publications.waset.org/abstracts/search?q=3D%20animation" title=" 3D animation"> 3D animation</a>, <a href="https://publications.waset.org/abstracts/search?q=RGB-D%20video" title=" RGB-D video"> RGB-D video</a>, <a href="https://publications.waset.org/abstracts/search?q=temporally%20coherent%203D%20animation" title=" temporally coherent 3D animation"> temporally coherent 3D animation</a> </p> <a href="https://publications.waset.org/abstracts/12034/temporally-coherent-3d-animation-reconstruction-from-rgb-d-video-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/12034.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">373</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">18804</span> Estimating Lost Digital Video Frames Using Unidirectional and Bidirectional Estimation Based on Autoregressive Time Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Navid%20Daryasafar">Navid Daryasafar</a>, <a href="https://publications.waset.org/abstracts/search?q=Nima%20Farshidfar"> Nima Farshidfar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, we make attempt to hide error in video with an emphasis on the time-wise use of autoregressive (AR) models. To resolve this problem, we assume that all information in one or more video frames is lost. Then, lost frames are estimated using analogous Pixels time information in successive frames. Accordingly, after presenting autoregressive models and how they are applied to estimate lost frames, two general methods are presented for using these models. The first method which is the same standard method of autoregressive models estimates lost frame in unidirectional form. Usually, in such condition, previous frames information is used for estimating lost frame. Yet, in the second method, information from the previous and next frames is used for estimating the lost frame. As a result, this method is known as bidirectional estimation. Then, carrying out a series of tests, performance of each method is assessed in different modes. And, results are compared. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=error%20steganography" title="error steganography">error steganography</a>, <a href="https://publications.waset.org/abstracts/search?q=unidirectional%20estimation" title=" unidirectional estimation"> unidirectional estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=bidirectional%20estimation" title=" bidirectional estimation"> bidirectional estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=AR%20linear%20estimation" title=" AR linear estimation"> AR linear estimation</a> </p> <a href="https://publications.waset.org/abstracts/14175/estimating-lost-digital-video-frames-using-unidirectional-and-bidirectional-estimation-based-on-autoregressive-time-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14175.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">540</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">18803</span> A Passive Digital Video Authentication Technique Using Wavelet Based Optical Flow Variation Thresholding</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=R.%20S.%20Remya">R. S. Remya</a>, <a href="https://publications.waset.org/abstracts/search?q=U.%20S.%20Sethulekshmi"> U. S. Sethulekshmi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Detecting the authenticity of a video is an important issue in digital forensics as Video is used as a silent evidence in court such as in child pornography, movie piracy cases, insurance claims, cases involving scientific fraud, traffic monitoring etc. The biggest threat to video data is the availability of modern open video editing tools which enable easy editing of videos without leaving any trace of tampering. In this paper, we propose an efficient passive method for inter-frame video tampering detection, its type and location by estimating the optical flow of wavelet features of adjacent frames and thresholding the variation in the estimated feature. The performance of the algorithm is compared with the z-score thresholding and achieved an efficiency above 95% on all the tested databases. The proposed method works well for videos with dynamic (forensics) as well as static (surveillance) background. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=discrete%20wavelet%20transform" title="discrete wavelet transform">discrete wavelet transform</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20flow" title=" optical flow"> optical flow</a>, <a href="https://publications.waset.org/abstracts/search?q=optical%20flow%20variation" title=" optical flow variation"> optical flow variation</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20tampering" title=" video tampering"> video tampering</a> </p> <a href="https://publications.waset.org/abstracts/45252/a-passive-digital-video-authentication-technique-using-wavelet-based-optical-flow-variation-thresholding" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/45252.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">359</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">18802</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">18801</span> Research on Evaluation Method of Urban Road Section Traffic Safety Status Based on Video Information</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiang%20Zhang">Qiang Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaojian%20Hu"> Xiaojian Hu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Aiming at the problem of the existing real-time evaluation methods for traffic safety status, a video information-based urban road section traffic safety status evaluation method was established, and the rapid detection method of traffic flow parameters based on video information is analyzed. The concept of the speed dispersion of the road section that affects the traffic safety state of the urban road section is proposed, and the method of evaluating the traffic safety state of the urban road section based on the speed dispersion of the road section is established. Experiments show that the proposed method can reasonably evaluate the safety status of urban roads in real-time, and the evaluation results can provide a corresponding basis for the traffic management department to formulate an effective urban road section traffic safety improvement plan. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=intelligent%20transportation%20system" title="intelligent transportation system">intelligent transportation system</a>, <a href="https://publications.waset.org/abstracts/search?q=road%20traffic%20safety" title=" road traffic safety"> road traffic safety</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20information" title=" video information"> video information</a>, <a href="https://publications.waset.org/abstracts/search?q=vehicle%20speed%20dispersion" title=" vehicle speed dispersion"> vehicle speed dispersion</a> </p> <a href="https://publications.waset.org/abstracts/138781/research-on-evaluation-method-of-urban-road-section-traffic-safety-status-based-on-video-information" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/138781.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">164</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=video%20time&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=video%20time&page=3">3</a></li> <li class="page-item"><a class="page-link" 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