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representation</title> <meta name="description" content="Search results for: video representation"> <meta name="keywords" content="video representation"> <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 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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="video representation"> <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> 2173</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: video representation</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2173</span> Human Behavior Modeling in Video Surveillance of Conference Halls </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nour%20Charara">Nour Charara</a>, <a href="https://publications.waset.org/abstracts/search?q=Hussein%20Charara"> Hussein Charara</a>, <a href="https://publications.waset.org/abstracts/search?q=Omar%20Abou%20Khaled"> Omar Abou Khaled</a>, <a href="https://publications.waset.org/abstracts/search?q=Hani%20Abdallah"> Hani Abdallah</a>, <a href="https://publications.waset.org/abstracts/search?q=Elena%20Mugellini"> Elena Mugellini</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, we present a human behavior modeling approach in videos scenes. This approach is used to model the normal behaviors in the conference halls. We exploited the Probabilistic Latent Semantic Analysis technique (PLSA), using the 'Bag-of-Terms' paradigm, as a tool for exploring video data to learn the model by grouping similar activities. Our term vocabulary consists of 3D spatio-temporal patch groups assigned by the direction of motion. Our video representation ensures the spatial information, the object trajectory, and the motion. The main importance of this approach is that it can be adapted to detect abnormal behaviors in order to ensure and enhance human security. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=activity%20modeling" title="activity modeling">activity modeling</a>, <a href="https://publications.waset.org/abstracts/search?q=clustering" title=" clustering"> clustering</a>, <a href="https://publications.waset.org/abstracts/search?q=PLSA" title=" PLSA"> PLSA</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20representation" title=" video representation"> video representation</a> </p> <a href="https://publications.waset.org/abstracts/70466/human-behavior-modeling-in-video-surveillance-of-conference-halls" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70466.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">394</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">2172</span> Virtual and Augmented Reality Based Heritage Gamification: Basilica of Smyrna in Turkey</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Tugba%20Saricaoglu">Tugba Saricaoglu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study argues about the potential representation and interpretation of Basilica of Smyrna through gamification. Representation can be defined as a key which plays a role as a converter in order to provide interpretation of something according to the person who perceives. Representation of cultural heritage is a hypothetical and factual approach in terms of its sustainable conservation. Today, both site interpreters and public of cultural heritage have varying perspectives due to their different demographic, social, and even cultural backgrounds. Additionally, gamification application offers diversion of methods suchlike video games to improve user perspective of non-game platforms, contexts, and issues. Hence, cultural heritage and video game decided to be analyzed. Moreover, there are basically different ways of representation of cultural heritage such as digital, physical, and virtual methods in terms of conservation. Virtual reality (VR) and augmented reality (AR) technologies are two of the contemporary digital methods of heritage conservation. In this study, 3D documented ruins of the Basilica will be presented in the virtual and augmented reality based technology as a theoretical gamification sample. Also, this paper will focus on two sub-topics: First, evaluation of the video-game platforms applied to cultural heritage sites, and second, potentials of cultural heritage to be represented in video game platforms. The former will cover the analysis of some case(s) with regard to the concepts and representational aspects of cultural heritage. The latter will include the investigation of cultural heritage sites which carry such a potential and their sustainable conversation. Consequently, after mutual collection of information from cultural heritage and video game platforms, a perspective will be provided in terms of interpretation of representation of cultural heritage by sampling that on Basilica of Smyrna by using VR and AR based technologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Basilica%20of%20Smyrna" title="Basilica of Smyrna">Basilica of Smyrna</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20heritage" title=" cultural heritage"> cultural heritage</a>, <a href="https://publications.waset.org/abstracts/search?q=digital%20heritage" title=" digital heritage"> digital heritage</a>, <a href="https://publications.waset.org/abstracts/search?q=gamification" title=" gamification"> gamification</a> </p> <a href="https://publications.waset.org/abstracts/57712/virtual-and-augmented-reality-based-heritage-gamification-basilica-of-smyrna-in-turkey" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/57712.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">466</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2171</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">2170</span> Authentication Based on Hand Movement by Low Dimensional Space Representation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Reut%20Lanyado">Reut Lanyado</a>, <a href="https://publications.waset.org/abstracts/search?q=David%20Mendlovic"> David Mendlovic</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Most biological methods for authentication require special equipment and, some of them are easy to fake. We proposed a method for authentication based on hand movement while typing a sentence with a regular camera. This technique uses the full video of the hand, which is harder to fake. In the first phase, we tracked the hand joints in each frame. Next, we represented a single frame for each individual using our Pose Agnostic Rotation and Movement (PARM) dimensional space. Then, we indicated a full video of hand movement in a fixed low dimensional space using this method: Fixed Dimension Video by Interpolation Statistics (FDVIS). Finally, we identified each individual in the FDVIS representation using unsupervised clustering and supervised methods. Accuracy exceeds 96% for 80 individuals by using supervised KNN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=authentication" title="authentication">authentication</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20extraction" title=" feature extraction"> feature extraction</a>, <a href="https://publications.waset.org/abstracts/search?q=hand%20recognition" title=" hand recognition"> hand recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=security" title=" security"> security</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20processing" title=" signal processing"> signal processing</a> </p> <a href="https://publications.waset.org/abstracts/112262/authentication-based-on-hand-movement-by-low-dimensional-space-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/112262.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2169</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">2168</span> Adversarial Disentanglement Using Latent Classifier for Pose-Independent Representation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Hamed%20Alqahtani">Hamed Alqahtani</a>, <a href="https://publications.waset.org/abstracts/search?q=Manolya%20Kavakli-Thorne"> Manolya Kavakli-Thorne</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The large pose discrepancy is one of the critical challenges in face recognition during video surveillance. Due to the entanglement of pose attributes with identity information, the conventional approaches for pose-independent representation lack in providing quality results in recognizing largely posed faces. In this paper, we propose a practical approach to disentangle the pose attribute from the identity information followed by synthesis of a face using a classifier network in latent space. The proposed approach employs a modified generative adversarial network framework consisting of an encoder-decoder structure embedded with a classifier in manifold space for carrying out factorization on the latent encoding. It can be further generalized to other face and non-face attributes for real-life video frames containing faces with significant attribute variations. Experimental results and comparison with state of the art in the field prove that the learned representation of the proposed approach synthesizes more compelling perceptual images through a combination of adversarial and classification losses. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=disentanglement" title="disentanglement">disentanglement</a>, <a href="https://publications.waset.org/abstracts/search?q=face%20detection" title=" face detection"> face detection</a>, <a href="https://publications.waset.org/abstracts/search?q=generative%20adversarial%20networks" title=" generative adversarial networks"> generative adversarial networks</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20surveillance" title=" video surveillance"> video surveillance</a> </p> <a href="https://publications.waset.org/abstracts/108319/adversarial-disentanglement-using-latent-classifier-for-pose-independent-representation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108319.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">129</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">2167</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">2166</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">2165</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">2164</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">2163</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">2162</span> A Co-Constructed Picture of Chinese Teachers' Conceptions of Learning at Play</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Shu-Chen%20Wu">Shu-Chen Wu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This qualitative study investigated Chinese teachers’ perspectives on learning at play. Six kindergarten teachers were interviewed to obtain their understanding of learning at play. Exemplary play episodes from their classrooms were selected with the assistance of the participating teachers. Four three-minute videos containing the largest amount of learning elements based on the teachers’ views were selected for analysis. Applying video-stimulated interviews, the selected video clips were shown to eight teachers in two focus groups to elicit their perspectives on learning at play. The findings revealed that Chinese teachers have a very structured representation of learning at play, which should contribute to the development of professional practices and curricular policies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=learning%20at%20play" title="learning at play">learning at play</a>, <a href="https://publications.waset.org/abstracts/search?q=teachers%E2%80%99%20perspectives" title=" teachers’ perspectives"> teachers’ perspectives</a>, <a href="https://publications.waset.org/abstracts/search?q=co-constructed%20views" title=" co-constructed views"> co-constructed views</a>, <a href="https://publications.waset.org/abstracts/search?q=video-stimulated%20interviews" title=" video-stimulated interviews"> video-stimulated interviews</a> </p> <a href="https://publications.waset.org/abstracts/80893/a-co-constructed-picture-of-chinese-teachers-conceptions-of-learning-at-play" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/80893.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">231</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">2161</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">2160</span> Violence Detection and Tracking on Moving Surveillance Video Using Machine Learning Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abe%20Degale%20D.">Abe Degale D.</a>, <a href="https://publications.waset.org/abstracts/search?q=Cheng%20Jian"> Cheng Jian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> When creating automated video surveillance systems, violent action recognition is crucial. In recent years, hand-crafted feature detectors have been the primary method for achieving violence detection, such as the recognition of fighting activity. Researchers have also looked into learning-based representational models. On benchmark datasets created especially for the detection of violent sequences in sports and movies, these methods produced good accuracy results. The Hockey dataset's videos with surveillance camera motion present challenges for these algorithms for learning discriminating features. Image recognition and human activity detection challenges have shown success with deep representation-based methods. For the purpose of detecting violent images and identifying aggressive human behaviours, this research suggested a deep representation-based model using the transfer learning idea. The results show that the suggested approach outperforms state-of-the-art accuracy levels by learning the most discriminating features, attaining 99.34% and 99.98% accuracy levels on the Hockey and Movies datasets, respectively. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=violence%20detection" title="violence detection">violence detection</a>, <a href="https://publications.waset.org/abstracts/search?q=faster%20RCNN" title=" faster RCNN"> faster RCNN</a>, <a href="https://publications.waset.org/abstracts/search?q=transfer%20learning%20and" title=" transfer learning and"> transfer learning and</a>, <a href="https://publications.waset.org/abstracts/search?q=surveillance%20video" title=" surveillance video"> surveillance video</a> </p> <a href="https://publications.waset.org/abstracts/171296/violence-detection-and-tracking-on-moving-surveillance-video-using-machine-learning-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/171296.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">2159</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">2158</span> Hardware Implementation of Local Binary Pattern Based Two-Bit Transform Motion Estimation </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Seda%20Yavuz">Seda Yavuz</a>, <a href="https://publications.waset.org/abstracts/search?q=An%C4%B1l%20%C3%87elebi"> Anıl Çelebi</a>, <a href="https://publications.waset.org/abstracts/search?q=Aysun%20Ta%C5%9Fyap%C4%B1%20%C3%87elebi"> Aysun Taşyapı Çelebi</a>, <a href="https://publications.waset.org/abstracts/search?q=O%C4%9Fuzhan%20Urhan"> Oğuzhan Urhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Nowadays, demand for using real-time video transmission capable devices is ever-increasing. So, high resolution videos have made efficient video compression techniques an essential component for capturing and transmitting video data. Motion estimation has a critical role in encoding raw video. Hence, various motion estimation methods are introduced to efficiently compress the video. Low bit‑depth representation based motion estimation methods facilitate computation of matching criteria and thus, provide small hardware footprint. In this paper, a hardware implementation of a two-bit transformation based low-complexity motion estimation method using local binary pattern approach is proposed. Image frames are represented in two-bit depth instead of full-depth by making use of the local binary pattern as a binarization approach and the binarization part of the hardware architecture is explained in detail. Experimental results demonstrate the difference between the proposed hardware architecture and the architectures of well-known low-complexity motion estimation methods in terms of important aspects such as resource utilization, energy and power consumption. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=binarization" title="binarization">binarization</a>, <a href="https://publications.waset.org/abstracts/search?q=hardware%20architecture" title=" hardware architecture"> hardware architecture</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20binary%20pattern" title=" local binary pattern"> local binary pattern</a>, <a href="https://publications.waset.org/abstracts/search?q=motion%20estimation" title=" motion estimation"> motion estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=two-bit%20transform" title=" two-bit transform"> two-bit transform</a> </p> <a href="https://publications.waset.org/abstracts/77730/hardware-implementation-of-local-binary-pattern-based-two-bit-transform-motion-estimation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77730.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">311</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">2157</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">2156</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">2155</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">2154</span> A Four-Step Ortho-Rectification Procedure for Geo-Referencing Video Streams from a Low-Cost UAV</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20O.%20Olawale">B. O. Olawale</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20R.%20Chatwin"> C. R. Chatwin</a>, <a href="https://publications.waset.org/abstracts/search?q=R.%20C.%20D.%20Young"> R. C. D. Young</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20M.%20Birch"> P. M. Birch</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20O.%20Faithpraise"> F. O. Faithpraise</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20O.%20Olukiran"> A. O. Olukiran</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Ortho-rectification is the process of geometrically correcting an aerial image such that the scale is uniform. The ortho-image formed from the process is corrected for lens distortion, topographic relief, and camera tilt. This can be used to measure true distances, because it is an accurate representation of the Earth’s surface. Ortho-rectification and geo-referencing are essential to pin point the exact location of targets in video imagery acquired at the UAV platform. This can only be achieved by comparing such video imagery with an existing digital map. However, it is only when the image is ortho-rectified with the same co-ordinate system as an existing map that such a comparison is possible. The video image sequences from the UAV platform must be geo-registered, that is, each video frame must carry the necessary camera information before performing the ortho-rectification process. Each rectified image frame can then be mosaicked together to form a seamless image map covering the selected area. This can then be used for comparison with an existing map for geo-referencing. In this paper, we present a four-step ortho-rectification procedure for real-time geo-referencing of video data from a low-cost UAV equipped with multi-sensor system. The basic procedures for the real-time ortho-rectification are: (1) Decompilation of video stream into individual frames; (2) Finding of interior camera orientation parameters; (3) Finding the relative exterior orientation parameters for each video frames with respect to each other; (4) Finding the absolute exterior orientation parameters, using self-calibration adjustment with the aid of a mathematical model. Each ortho-rectified video frame is then mosaicked together to produce a 2-D planimetric mapping, which can be compared with a well referenced existing digital map for the purpose of georeferencing and aerial surveillance. A test field located in Abuja, Nigeria was used for testing our method. Fifteen minutes video and telemetry data were collected using the UAV and the data collected were processed using the four-step ortho-rectification procedure. The results demonstrated that the geometric measurement of the control field from ortho-images are more reliable than those from original perspective photographs when used to pin point the exact location of targets on the video imagery acquired by the UAV. The 2-D planimetric accuracy when compared with the 6 control points measured by a GPS receiver is between 3 to 5 meters. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=geo-referencing" title="geo-referencing">geo-referencing</a>, <a href="https://publications.waset.org/abstracts/search?q=ortho-rectification" title=" ortho-rectification"> ortho-rectification</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20frame" title=" video frame"> video frame</a>, <a href="https://publications.waset.org/abstracts/search?q=self-calibration" title=" self-calibration"> self-calibration</a> </p> <a href="https://publications.waset.org/abstracts/33730/a-four-step-ortho-rectification-procedure-for-geo-referencing-video-streams-from-a-low-cost-uav" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/33730.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">478</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">2153</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">2152</span> The Representation of J. D. Salinger’s Views on Changes in American Society in the 1940s in The Catcher in the Rye</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jessadaporn%20Achariyopas">Jessadaporn Achariyopas</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The objectives of this study aim to analyze both the protagonist in The Catcher in the Rye in terms of ideological concepts and narrative techniques which influence the construction of the representation and the relationship between the representation and J. D. Salinger’s views on changes in American society in the 1940s. This area of study might concern two theories: namely, a theory of representation and narratology. In addition, this research is intended to answer the following three questions. Firstly, how is the production of meaning through language in The Catcher in the Rye constructed? Secondly, what are J. D. Salinger’s views on changes in American society in the 1940s? Lastly, how is the relationship between the representation and J. D. Salinger’s views? The findings showed that the protagonist’s views, J. D. Salinger’s views, and changes in American society in the 1940s are obviously interrelated. The production of meaning which is the representation of the protagonist’s views was constructed of narrative techniques. J. D. Salinger’s views on changes in American society in the 1940s were the same antisocial perspectives as Holden Caulfield’s which are phoniness, alienation and meltdown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=representation" title="representation">representation</a>, <a href="https://publications.waset.org/abstracts/search?q=construction%20of%20the%20representation" title=" construction of the representation"> construction of the representation</a>, <a href="https://publications.waset.org/abstracts/search?q=systems%20of%20representation" title=" systems of representation"> systems of representation</a>, <a href="https://publications.waset.org/abstracts/search?q=phoniness" title=" phoniness"> phoniness</a>, <a href="https://publications.waset.org/abstracts/search?q=alienation" title=" alienation"> alienation</a>, <a href="https://publications.waset.org/abstracts/search?q=meltdown" title=" meltdown"> meltdown</a> </p> <a href="https://publications.waset.org/abstracts/9035/the-representation-of-j-d-salingers-views-on-changes-in-american-society-in-the-1940s-in-the-catcher-in-the-rye" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9035.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">2151</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">2150</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">2149</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">2148</span> Digital Musical Organology: The Audio Games: The Question of “A-Musicological” Interfaces</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Herv%C3%A9%20Z%C3%A9nouda">Hervé Zénouda</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This article seeks to shed light on an emerging creative field: "Audio games," at the crossroads between video games and computer music. Indeed, many applications, which propose entertaining audio-visual experiences with the objective of musical creation, are available today for different supports (game consoles, computers, cell phones). The originality of this field is the use of the gameplay of video games applied to music composition. Thus, composing music using interfaces but also cognitive logics that we qualify as "a-musicological" seem to us particularly interesting from the perspective of musical digital organology. This field raises questions about the representation of sound and musical structures and develops new instrumental gestures and strategies of musical composition. We will try in this article to define the characteristics of this field by highlighting some historical milestones (abstract cinema, game theory in music, actions, and graphic scores) as well as the novelties brought by digital technologies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=audio-games" title="audio-games">audio-games</a>, <a href="https://publications.waset.org/abstracts/search?q=video%20games" title=" video games"> video games</a>, <a href="https://publications.waset.org/abstracts/search?q=computer%20generated%20music" title=" computer generated music"> computer generated music</a>, <a href="https://publications.waset.org/abstracts/search?q=gameplay" title=" gameplay"> gameplay</a>, <a href="https://publications.waset.org/abstracts/search?q=interactivity" title=" interactivity"> interactivity</a>, <a href="https://publications.waset.org/abstracts/search?q=synesthesia" title=" synesthesia"> synesthesia</a>, <a href="https://publications.waset.org/abstracts/search?q=sound%20interfaces" title=" sound interfaces"> sound interfaces</a>, <a href="https://publications.waset.org/abstracts/search?q=relationships%20image%2Fsound" title=" relationships image/sound"> relationships image/sound</a>, <a href="https://publications.waset.org/abstracts/search?q=audiovisual%20music" title=" audiovisual music"> audiovisual music</a> </p> <a href="https://publications.waset.org/abstracts/152518/digital-musical-organology-the-audio-games-the-question-of-a-musicological-interfaces" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152518.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">112</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">2147</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">2146</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">2145</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">2144</span> Content Analysis of Video Translations: Examining the Linguistic and Thematic Approach by Translator Abdullah Khrief on the X Platform</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Easa%20Almustanyir">Easa Almustanyir</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study investigates the linguistic and thematic approach of translator Abdullah Khrief in the context of video translations on the X platform. The sample comprises 15 videos from Khrief's account, covering diverse content categories like science, religion, social issues, personal experiences, lifestyle, and culture. The analysis focuses on two aspects: language usage and thematic representation. Regarding language, the study examines the prevalence of English while considering the inclusion of French and German content, highlighting Khrief's multilingual versatility and ability to navigate cultural nuances. Thematically, the study explores the diverse range of topics covered, encompassing scientific, religious, social, and personal narratives, underscoring Khrief's broad subject matter expertise and commitment to knowledge dissemination. The study employs a mixed-methods approach, combining quantitative data analysis with qualitative content analysis. Statistical data on video languages, presenter genders, and content categories are analyzed, and a thorough content analysis assesses translation accuracy, cultural appropriateness, and overall quality. Preliminary findings indicate a high level of professionalism and expertise in Khrief's translations. The absence of errors across the diverse range of videos establishes his credibility and trustworthiness. Furthermore, the accurate representation of cultural nuances and sensitive topics highlights Khrief's cultural sensitivity and commitment to preserving intended meanings and emotional resonance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=audiovisual%20translation" title="audiovisual translation">audiovisual translation</a>, <a href="https://publications.waset.org/abstracts/search?q=linguistic%20versatility" title=" linguistic versatility"> linguistic versatility</a>, <a href="https://publications.waset.org/abstracts/search?q=thematic%20diversity" title=" thematic diversity"> thematic diversity</a>, <a href="https://publications.waset.org/abstracts/search?q=cultural%20sensitivity" title=" cultural sensitivity"> cultural sensitivity</a>, <a href="https://publications.waset.org/abstracts/search?q=content%20analysis" title=" content analysis"> content analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=mixed-methods%20approach" title=" mixed-methods approach"> mixed-methods approach</a> </p> <a href="https://publications.waset.org/abstracts/192262/content-analysis-of-video-translations-examining-the-linguistic-and-thematic-approach-by-translator-abdullah-khrief-on-the-x-platform" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/192262.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">17</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%20representation&page=2">2</a></li> <li class="page-item"><a class="page-link" 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