Relevant bibliographies by topics / Videos analytics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Videos analytics'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Videos analytics"

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Jain, Puneet, Justin Manweiler, Arup Acharya, and Romit Roy Choudhury. "Scalable Social Analytics for Live Viral Event Prediction." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (May 16, 2014): 226–35. http://dx.doi.org/10.1609/icwsm.v8i1.14504.

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Large-scale, predictive social analytics have proven effective. Over the last decade, research and industrial efforts have understood the potential value of inferences based on online behavior analysis, sentiment mining, influence analysis, epidemic spread, etc. The majority of these efforts, however, are not yet designed with realtime responsiveness as a first-order requirement. Typical systems perform a post-mortem analysis on volumes of historical data and validate their “predictions” against already-occurred events.We observe that in many applications, real-time predictions are critical and delays of hours (and even minutes) can reduce their utility. As examples: political campaigns could react very quickly to a scandal spreading on Facebook; content distribution networks (CDNs) could prefetch videos that are predicted to soon go viral; online advertisement campaigns can be corrected to enhance consumer reception. This paper proposes CrowdCast, a cloud-based framework to enable real-time analysis and prediction from streaming social data. As an instantiation of this framework, we tune CrowdCast to observe Twitter tweets, and predict which YouTube videos are most likely to “go viral” in the near future. To this end, CrowdCast first applies online machine learning to map natural language tweets to a specific YouTube video. Then, tweets that indeed refer to videos are weighted by the perceived “influence” of the sender. Finally, the video’s spread is predicted through a sociological model, derived from the emerging structure of the graph over which the video-related tweets are (still) spreading. Combining metrics of influence and live structure, CrowdCast outputs sets of candidate videos, identified as likely to become viral in the next few hours. We monitor Twitter for more than 30 days, and find that CrowdCast’s real-time predictions demonstrate encouraging correlation with actual YouTube viewership in the near future.
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Arulraj, Joy. "Accelerating Video Analytics." ACM SIGMOD Record 50, no. 4 (January 31, 2022): 39–40. http://dx.doi.org/10.1145/3516431.3516442.

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MOTIVATION. The advent of inexpensive, high-quality cameras has led to a rapid increase in the volume of generated video data [19, 16]. It is now feasible to automatically analyze these video datasets at scale due to two developments over the last decade. First, researchers have designed complex, computationally-intensive deep learning (DL) models that capture the contents of a given set of video frames (e.g., objects present in a particular frame [11]) [15]. Second, the computational capabilities of hardware accelerators for evaluating these DL models have increased over the last decade (e.g., TPUs) [8]. We anticipate that automated analysis of videos will reduce the labor cost of analyzing video
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Wang, Han, Shangyu Xie, and Yuan Hong. "VideoDP: A Flexible Platform for Video Analytics with Differential Privacy." Proceedings on Privacy Enhancing Technologies 2020, no. 4 (October 1, 2020): 277–96. http://dx.doi.org/10.2478/popets-2020-0073.

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AbstractMassive amounts of videos are ubiquitously generated in personal devices and dedicated video recording facilities. Analyzing such data would be extremely beneficial in real world (e.g., urban traffic analysis). However, videos contain considerable sensitive information, such as human faces, identities and activities. Most of the existing video sanitization techniques simply obfuscate the video by detecting and blurring the region of interests (e.g., faces, vehicle plates, locations and timestamps). Unfortunately, privacy leakage in the blurred video cannot be effectively bounded, especially against unknown background knowledge. In this paper, to our best knowledge, we propose the first differentially private video analytics platform (VideoDP) which flexibly supports different video analyses with rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Finally, we conduct experiments on real videos, and the experimental results demonstrate that VideoDP can generate accurate results for video analytics.
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Dolores, Maria, and Jorge Mañana-Rodriguez. "Exploring Engagement in Online Videos for Language Learning through YouTube’s Learning Analytics." EDEN Conference Proceedings, no. 1 (September 21, 2021): 49–58. http://dx.doi.org/10.38069/edenconf-2021-ac0005.

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Until a few years ago, video analytics were not accessible to learning stakeholders, mainly because online video platforms did not share the users’ interactions on the system with stakeholders. However, this scenario has changed, and currently YouTube, the world’s largest media sharing site, offers these data. YouTube is also the main tool for transmitting audio-visual content in Language MOOCs (massive open online courses), and its video engagement data can be monitored through the YouTube Studio channel, which provides free and open access to video analytics. In this paper we present our research based on the analysis of viewers’ engagement with 35 videos of the Language MOOC entitled Alemán para hispanohablantes: basic principles (German for Spanish-speakers). The data provided by the YouTube Studio Learning Analytics platform has enabled new insights related to participants’ watching of these videos in Language MOOCs (LMOOCs). The results of our study provide pedagogical implications for Foreign Language instructors concerning the use of videos in language learning.
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Zhang, Jingjing, Yicheng Huang, and Ming Gao. "Video Features, Engagement, and Patterns of Collective Attention Allocation." Journal of Learning Analytics 9, no. 1 (March 11, 2022): 32–52. http://dx.doi.org/10.18608/jla.2022.7421.

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Network analytics has the potential to examine new behaviour patterns that are often hidden by the complexity of online interactions. One of the varied network analytics approaches and methods, the model of collective attention, takes an ecological system perspective to exploring the dynamic process of participation patterns in online and flexible learning environments. This study selected “Fundamentals of C++ programming (Spring 2019)” on XuetangX as an example through which to observe the allocation patterns of attention within MOOC videos, as well as how video features and engagement correlate with the accumulation, circulation, and dissipation pattern of collective attention. The results showed that the types of instructions in videos predicted attention allocation patterns, but they did not predict the engagement of video watching. Instead, the length and whether the full screen was used in the videos had a strong impact on engagement. Learners were more likely to reach a high level of engagement in video watching when their attention had been circulated around the videos. The results imply that understanding the patterns and dynamics of attention flow and how learners engage with videos will allow us to design cost-effective learning resources to prevent learners from becoming overloaded.
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Carpenter, Chris. "Computer Vision Analytics Enables Determination of Rig State." Journal of Petroleum Technology 74, no. 01 (January 1, 2022): 96–98. http://dx.doi.org/10.2118/0122-0096-jpt.

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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 204086, “Determining Rig State From Computer Vision Analytics,” by Crispin Chatar, SPE, and Suhas Suresha, Schlumberger, and Laetitia Shao, Stanford University, et al. The paper has not been peer reviewed. While companies cannot agree on a standard definition of “rig state,” they can agree that, as further use is made of remote operations and automation, rig-state calculation is mandatory in some form. By use of a machine-learning model that relies exclusively on videos collected on the rig floor to infer rig states, overcoming the limitations of existing methods is possible as the industry moves into a future of rigs featuring advanced technologies. Introduction The complete paper presents a machine-learning pipeline implemented to determine rig state from videos captured on the floor of an operating rig. The pipeline is composed of two parts. First, the annotation pipeline matches each frame of the video data set to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Second, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from the videos. The models are trained on a data set of 3 million frames on a cloud platform using graphics processing units. Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional 3D (C3D) model, and a two-stream model that uses optical flow to capture temporal information.
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Liu, Ying, Dickson K. W. Chiu, and Kevin K. W. Ho. "Short-Form Videos for Public Library Marketing: Performance Analytics of Douyin in China." Applied Sciences 13, no. 6 (March 7, 2023): 3386. http://dx.doi.org/10.3390/app13063386.

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Short-form video platforms have become an important marketing channel for library resources and services. However, such promotions’ actual performance is not as good as expected. This research examined the performance of library marketing on the dominant short-form video platform in China, Douyin (aka TikTok worldwide), with social media analytics, including topic and correlation analysis. Results indicated that the number of fans of an account is positively correlated with the number of likes (p < 0.001) and independent of the number of videos (p > 0.05). Libraries post videos most often on the topic of “Reading Promotion”(31%), but the marketing performance on this topic is average (Mean DMI = 90.27). The most popular topics for patrons are “Hot Topics” and “Knowledge Quiz” (Mean DMI = 207.00). Using short-form videos for library marketing is a new practice, and scant studies have evaluated such performance, especially in Asia. Our results strengthen library practitioners’ awareness and reflections on conducting new media services and short-form video promotion, especially for the newer generation.
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Luo, Zhiming, Pierre-Marc Jodoin, Song-Zhi Su, Shao-Zi Li, and Hugo Larochelle. "Traffic Analytics With Low-Frame-Rate Videos." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 4 (April 2018): 878–91. http://dx.doi.org/10.1109/tcsvt.2016.2632439.

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DAUN, Felipe, and Ana Maria Dianezi GAMBARDELLA. "Educational videos with nutritional approach in YouTube." Revista de Nutrição 31, no. 3 (May 2018): 339–49. http://dx.doi.org/10.1590/1678-98652018000300007.

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ABSTRACT Objective Produce food and nutrition education videos, post these on YouTube and evaluate their reception over a two-year period. Methods Afterward bibliographic searches, sixteen different themes were developed and explored. An educational objective was defined for each video, took into account food and nutrition aspects in Brazil. The reception of the videos was evaluated using the “YouTube Analytics” tool, which allows analysis of the number of times videos were played, average playing time, and profile of the viewers. Results Sixteen videos were produced from November 2013 to July 2015. Views for each video within two years of posting were calculated individually, giving a total of 78,546 views for all videos. Most of the videos delivered their educational message before the audience lost interest. Conclusion Videos successfully reached the YouTube users and delivered the food and nutrition education messages. Therefore, this pioneering work showed YouTube as a new setting for health promotion in Brazil, paving the way for further initiatives with this platform.
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Xiao, Sihan. "More than Data: A Multivocal Inquiry into Video-Based Research on Learning and Teaching." ECNU Review of Education 1, no. 3 (September 2018): 23–35. http://dx.doi.org/10.30926/ecnuroe2018010302.

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Purpose This commentary aims to echo Wilkinson, Bailey, and Maher's (this volume) arguments about the affordances of videos and video databases in studying learning and teaching. Design/Approach/Methods This article illustrates a multivocal approach to the videos from the Video Mosaic Collaborative (VMC). In particular, three mathematics teachers in Shanghai were invited to watch and discuss a set of VMC videos. Two recurring themes concerning mathematics learning and teaching were identified in this video-cued interview and discussed in relation to the VMC Analytics. Findings The VMC videos played a mediating and facilitating role in the interview, helping the teachers notice and reflect on the mundane, implicit culture practices. Based upon this analysis, I argue that to tap into the potential of video in educational research, we need to see videos as more than data and look for more possibilities of using them. Originality/Value To open and further research dialogues, this article discusses future directions of using videos in educational research and serves as an invitation to creative explorations, in-depth conversations, ethical reflections, and cross-cultural collaborations on the use of videos in education.
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Dissertations / Theses on the topic "Videos analytics"

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Abdallah, Raed. "Intelligent crime detection and behavioral pattern mining : a comprehensive study." Electronic Thesis or Diss., Université Paris Cité, 2023. http://www.theses.fr/2023UNIP7031.

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Face à l'évolution rapide du paysage criminel, les agences de maintien de l'ordre (LEA) sont confrontées à des défis croissants dans les enquêtes criminelles contemporaines. Cette thèse de doctorat entreprend une exploration transformative, stimulée par la nécessité urgente de révolutionner les méthodologies d'enquête et d'armer les LEA avec des outils de pointe pour lutter efficacement contre la criminalité. Ancré dans cette motivation impérative, ce travail de recherche navigue méticuleusement à travers diverses sources de données, y compris le réseau complexe des médias sociaux, les systèmes de surveillance vidéo omniprésents et les plateformes en ligne expansives, reconnaissant leur rôle fondamental dans la détection moderne du crime. L'étude vise à doter les LEA de capacités avancées en matière de détection intelligente du crime, compte tenu de la montée en puissance des interactions numériques. Les chercheurs explorent les complexités des médias sociaux, des vidéos de surveillance et des données en ligne, mettant l'accent sur la nécessité de renforcer les stratégies de maintien de l'ordre avec des solutions technologiques de pointe. La thèse présente trois objectifs pivots : La thèse a trois objectifs clés : automatiser l'identification des suspects en utilisant la science des données, les outils big data et les modèles ontologiques ; réaliser une analyse en temps réel des médias sociaux pour détecter rapidement les crimes dans le bruit numérique en utilisant des modèles sophistiqués ; améliorer la surveillance vidéo en intégrant des algorithmes de deep learning pour une détection rapide et précise des crimes liés aux couteaux, marquant une avancée significative dans la technologie de surveillance. Naviguer dans ce domaine de recherche présente des défis significatifs, notamment l'intégration de données hétérogènes et le développement de techniques de prétraitement efficaces. L'analyse en temps réel des subtilités des médias sociaux exige des modèles ontologiques compétents. La conception des systèmes de surveillance vidéo intelligents nécessite la fusion d'algorithmes de deep learning de pointe avec un traitement vidéo en temps réel, garantissant à la fois la rapidité et la précision dans la détection des crimes. Cette thèse présente des solutions novatrices pour la détection criminelle moderne. À travers ICAD, un système intelligent d'analyse et de détection en temps réel, les enquêtes sont automatisées et rationalisées. CRI-MEDIA, un cadre ontologique, permet une détection précise des crimes sur les médias sociaux. De plus, la recherche se penche sur la surveillance vidéo des crimes liés aux couteaux avec SVSS, intégrant des modèles de deep learning avancés. Cette intégration révolutionne les méthodes d'enquête, élevant les capacités des agences de maintien de l'ordre face à la complexité du crime numérique. Le texte complet comprend 1 235 caractères, espaces inclus. La validation expérimentale dans des scénarios criminels réels est essentielle pour garantir l'intégrité de la recherche. Les méthodologies sont rigoureusement testées dans des situations authentiques, utilisant des données provenant d'enquêtes réelles. Ces expériences confirment l'efficacité des solutions proposées, tout en fournissant des insights précieux pour des améliorations futures. Les résultats mettent en lumière l'applicabilité pratique de ces méthodes, leur flexibilité dans divers contextes de maintien de l'ordre et leur contribution à la sécurité publique
In the face of a rapidly evolving criminal landscape, law enforcement agencies (LEAs) grapple with escalating challenges in contemporary criminal investigations. This PhD thesis embarks on a transformative exploration, encouraged by an urgent need to revolutionize investigative methodologies and arm LEAs with state-of-the-art tools to combat crime effectively. Rooted in this imperative motivation, the research meticulously navigates diverse data sources, including the intricate web of social media networks, omnipresent video surveillance systems, and expansive online platforms, recognizing their fundamental roles in modern crime detection. The contextual backdrop of this research is the pressing demand to empower LEAs with advanced capabilities in intelligent crime detection. The surge in digital interactions necessitates a paradigm shift, compelling researchers to delve deep into the labyrinth of social media, surveillance footage, and online data. This context underscores the urgency to fortify law enforcement strategies with cutting-edge technological solutions. Motivated by urgency, the thesis focuses on three core objectives: firstly, automating suspect identification through the integration of data science, big data tools, and ontological models, streamlining investigations and empowering law enforcement with advanced inference rules; secondly, enabling real-time detection of criminal events within digital noise via intricate ontological models and advanced inference rules, providing actionable intelligence and supporting informed decision-making for law enforcement; and thirdly, enhancing video surveillance by integrating advanced deep learning algorithms for swift and precise detection of knife-related crimes, representing a pioneering advancement in video surveillance technology. Navigating this research terrain poses significant challenges. The integration of heterogeneous data demands robust preprocessing techniques, enabling the harmonious fusion of disparate data types. Real-time analysis of social media intricacies necessitates ontological models adept at discerning subtle criminal nuances within the digital tapestry. Moreover, designing Smart Video Surveillance Systems necessitates the fusion of state-of-the-art deep learning algorithms with real-time video processing, ensuring both speed and precision in crime detection. Against these challenges, the thesis contributes innovative solutions at the forefront of contemporary crime detection technology. The research introduces ICAD, an advanced framework automating suspect identification and revolutionizing investigations. CRI-MEDIA tackles social media crime challenges using a streamlined process and enriched criminal ontology. Additionally, SVSS, a Smart Video Surveillance System, swiftly detects knife-related crimes, enhancing public safety. Integrating ICAD, CRI-MEDIA, and SVSS, this work pioneers intelligent crime detection, empowering law enforcement with unprecedented capabilities in the digital age. Critical to the integrity of the research, the proposed methodologies undergo rigorous experimentation in authentic criminal scenarios. Real-world data gathered from actual investigations form the crucible wherein ICAD, CRI-MEDIA, and SVSS are tested. These experiments serve as a litmus test, affirming not only the viability of the proposed solutions but also offering nuanced insights for further refinement. The results underscore the practical applicability of these methodologies, their adaptability in diverse law enforcement contexts, and their role in enhancing public safety and security
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Carpani, Valerio. "CNN-based video analytics." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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The content of this thesis illustrates the six months work done during my internship at TKH Security Solutions - Siqura B.V. in Gouda, Netherlands. The aim of this thesis is to investigate on convolutional neural networks possible usage, from two different point of view: first we propose a novel algorithm for person re-identification, second we propose a deployment chain, for bringing research concepts to product ready solutions. In existing works, the person re-identification task is assumed to be independent of the person detection task. In this thesis instead, we consider the two tasks as linked. In fact, features produced by an object detection convolutional neural network (CNN) contain useful information, which is not being used by current re-identification methods. We propose several solutions for learning a metric on CNN features to distinguish between different identities. Then the best of these solutions is compared with state of the art alternatives on the popular Market-1501 dataset. Results show that our method outperforms them in computational efficiency, with only a reasonable loss in accuracy. For this reason, we believe that the proposed method can be more appropriate than current state of the art methods in situations where the computational efficiency is critical, such as embedded applications. The deployment chain we propose in this thesis has two main goals: it must be flexible for introducing new advancement in networks architecture, and it must be able to deploy neural networks both on server and embedded platforms. We tested several frameworks on several platforms and we ended up with a deployment chain that relies on the open source format ONNX.
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Pettersson, Johan, and Robin Veteläinen. "A comparison of solutions to measure Quality of Service for video streams." Thesis, KTH, Data- och elektroteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-188514.

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There are more and more people watching video streams over the Internet, and this has led to an increase in companies that compete for viewers. To improve the users experience, these companies can measure how their services are performing. The aim of this thesis was to recommend a way to measure the quality of service for a real time video streaming service. Three methods were presented; to buy the information from a content delivery network, extend existing analytics software or build a custom solution using packet sniffing. It was decided to extend existing analytics software. An evaluation was made on which software to extend. Four solutions were compared: Google Analytics, Mixpanel, Ooyala IQ and Piwik. The comparison was made using the analytic hierarchy process, comparing each alternative in their performance in criteria such as API maturity, flexibility, visualization and support. The recommended software to extend when building a real time video streaming service is Ooyala IQ which excel at flexibility and is easy to implement into existing solutions. It also had great capacity, offering no limit on how many events it can track per month, and finally it offers great dedicated support via telephone or email.
Det finns fler och fler personer som tittar på video strömmar på Internet, detta har lett till att nya företag har startats som konkurerar om tittare. För att förbättra kundupplevelsen kan man mäta hur tjänsten presterar. Målet med examensarbetet var att rekommendera hur man kan mäta tjänstekvalite för en realtidsvideoströmningstjänst. Tre olika lösningsförslag presenterades; att köpa informationen från en content delivery network, att bygga vidare på tillgängliga analytisk mjukvara eller att bygga ett eget paketsniffarprogram. Det bestämdes att bygga vidare på tillgänglig analytisk mjukvara. Fyra olika mjukvara jämfördes: Google Analytics, Mixpanel, Ooyala IQ och Piwik. Jämförelsen gjordes med hjälp av analytical hierarchy process, de olika alternativen jämfördes med avseende på: hur moget API:et var, flexibilitet, visualiseringen av data och support. Rekommendationen är att använda sig av Ooyala IQ som utmärker sig med avseende på flexibilitet, det var enkelt att använda deras API i sin egen lösning, det fanns ingen gräns på hur många händelser man kunde lagra per månad, och slutligen så fanns det dedikerad supportpersonal att nå via telefon eller email.
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Hassan, Waqas. "Video analytics for security systems." Thesis, University of Sussex, 2013. http://sro.sussex.ac.uk/id/eprint/43406/.

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This study has been conducted to develop robust event detection and object tracking algorithms that can be implemented in real time video surveillance applications. The aim of the research has been to produce an automated video surveillance system that is able to detect and report potential security risks with minimum human intervention. Since the algorithms are designed to be implemented in real-life scenarios, they must be able to cope with strong illumination changes and occlusions. The thesis is divided into two major sections. The first section deals with event detection and edge based tracking while the second section describes colour measurement methods developed to track objects in crowded environments. The event detection methods presented in the thesis mainly focus on detection and tracking of objects that become stationary in the scene. Objects such as baggage left in public places or vehicles parked illegally can cause a serious security threat. A new pixel based classification technique has been developed to detect objects of this type in cluttered scenes. Once detected, edge based object descriptors are obtained and stored as templates for tracking purposes. The consistency of these descriptors is examined using an adaptive edge orientation based technique. Objects are tracked and alarm events are generated if the objects are found to be stationary in the scene after a certain period of time. To evaluate the full capabilities of the pixel based classification and adaptive edge orientation based tracking methods, the model is tested using several hours of real-life video surveillance scenarios recorded at different locations and time of day from our own and publically available databases (i-LIDS, PETS, MIT, ViSOR). The performance results demonstrate that the combination of pixel based classification and adaptive edge orientation based tracking gave over 95% success rate. The results obtained also yield better detection and tracking results when compared with the other available state of the art methods. In the second part of the thesis, colour based techniques are used to track objects in crowded video sequences in circumstances of severe occlusion. A novel Adaptive Sample Count Particle Filter (ASCPF) technique is presented that improves the performance of the standard Sample Importance Resampling Particle Filter by up to 80% in terms of computational cost. An appropriate particle range is obtained for each object and the concept of adaptive samples is introduced to keep the computational cost down. The objective is to keep the number of particles to a minimum and only to increase them up to the maximum, as and when required. Variable standard deviation values for state vector elements have been exploited to cope with heavy occlusion. The technique has been tested on different video surveillance scenarios with variable object motion, strong occlusion and change in object scale. Experimental results show that the proposed method not only tracks the object with comparable accuracy to existing particle filter techniques but is up to five times faster. Tracking objects in a multi camera environment is discussed in the final part of the thesis. The ASCPF technique is deployed within a multi-camera environment to track objects across different camera views. Such environments can pose difficult challenges such as changes in object scale and colour features as the objects move from one camera view to another. Variable standard deviation values of the ASCPF have been utilized in order to cope with sudden colour and scale changes. As the object moves from one scene to another, the number of particles, together with the spread value, is increased to a maximum to reduce any effects of scale and colour change. Promising results are obtained when the ASCPF technique is tested on live feeds from four different camera views. It was found that not only did the ASCPF method result in the successful tracking of the moving object across different views but also maintained the real time frame rate due to its reduced computational cost thus indicating that the method is a potential practical solution for multi camera tracking applications.
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Asif, Muhammad. "Video analytics for intelligent surveillance systems." Thesis, University of Strathclyde, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530322.

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Höferlin, Benjamin [Verfasser]. "Scalable Visual Analytics for Video Surveillance / Benjamin Höferlin." München : Verlag Dr. Hut, 2014. http://d-nb.info/1050331842/34.

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Cheng, Guangchun. "Video Analytics with Spatio-Temporal Characteristics of Activities." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc799541/.

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As video capturing devices become more ubiquitous from surveillance cameras to smart phones, the demand of automated video analysis is increasing as never before. One obstacle in this process is to efficiently locate where a human operator’s attention should be, and another is to determine the specific types of activities or actions without ambiguity. It is the special interest of this dissertation to locate spatial and temporal regions of interest in videos and to develop a better action representation for video-based activity analysis. This dissertation follows the scheme of “locating then recognizing” activities of interest in videos, i.e., locations of potentially interesting activities are estimated before performing in-depth analysis. Theoretical properties of regions of interest in videos are first exploited, based on which a unifying framework is proposed to locate both spatial and temporal regions of interest with the same settings of parameters. The approach estimates the distribution of motion based on 3D structure tensors, and locates regions of interest according to persistent occurrences of low probability. Two contributions are further made to better represent the actions. The first is to construct a unifying model of spatio-temporal relationships between reusable mid-level actions which bridge low-level pixels and high-level activities. Dense trajectories are clustered to construct mid-level actionlets, and the temporal relationships between actionlets are modeled as Action Graphs based on Allen interval predicates. The second is an effort for a novel and efficient representation of action graphs based on a sparse coding framework. Action graphs are first represented using Laplacian matrices and then decomposed as a linear combination of primitive dictionary items following sparse coding scheme. The optimization is eventually formulated and solved as a determinant maximization problem, and 1-nearest neighbor is used for action classification. The experiments have shown better results than existing approaches for regions-of-interest detection and action recognition.
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Luo, Ning. "A Wireless Traffic Surveillance System Using Video Analytics." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc68005/.

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Video surveillance systems have been commonly used in transportation systems to support traffic monitoring, speed estimation, and incident detection. However, there are several challenges in developing and deploying such systems, including high development and maintenance costs, bandwidth bottleneck for long range link, and lack of advanced analytics. In this thesis, I leverage current wireless, video camera, and analytics technologies, and present a wireless traffic monitoring system. I first present an overview of the system. Then I describe the site investigation and several test links with different hardware/software configurations to demonstrate the effectiveness of the system. The system development process was documented to provide guidelines for future development. Furthermore, I propose a novel speed-estimation analytics algorithm that takes into consideration roads with slope angles. I prove the correctness of the algorithm theoretically, and validate the effectiveness of the algorithm experimentally. The experimental results on both synthetic and real dataset show that the algorithm is more accurate than the baseline algorithm 80% of the time. On average the accuracy improvement of speed estimation is over 3.7% even for very small slope angles.
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Barracu, Maria Antonietta. "Tecniche, metodologie e strumenti per la Web Analytics, con particolare attenzione sulla Video Analytics." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2011. http://amslaurea.unibo.it/1919/.

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In questa tesi viene affrontato il tema del tracciamento video, analizzando le principali tecniche, metodologie e strumenti per la video analytics. L'intero lavoro, è stato svolto interamente presso l'azienda BitBang, dal reperimento di informazioni e materiale utile, fino alla stesura dell'elaborato. Nella stessa azienda ho avuto modo di svolgere il tirocinio, durante il quale ho approfondito gli aspetti pratici della web e video analytics, osservando il lavoro sul campo degli specialisti del settore e acquisendo familiarità con gli strumenti di analisi dati tramite l'utilizzo delle principali piattaforme di web analytics. Per comprendere a pieno questo argomento, è stato necessario innanzitutto conoscere la web analytics di base. Saranno illustrate quindi, le metodologie classiche della web analytics, ovvero come analizzare il comportamento dei visitatori nelle pagine web con le metriche più adatte in base alle diverse tipologie di business, fino ad arrivare alla nuova tecnica di tracciamento eventi. Questa nasce subito dopo la diffusione nelle pagine dei contenuti multimediali, i quali hanno portato a un cambiamento nelle modalità di navigazione degli utenti e, di conseguenza, all'esigenza di tracciare le nuove azioni generate su essi, per avere un quadro completo dell'esperienza dei visitatori sul sito. Non sono più sufficienti i dati ottenuti con i tradizionali metodi della web analytics, ma è necessario integrarla con tecniche nuove, indispensabili se si vuole ottenere una panoramica a 360 gradi di tutto ciò che succede sul sito. Da qui viene introdotto il tracciamento video, chiamato video analytics. Verranno illustrate le principali metriche per l'analisi, e come sfruttarle al meglio in base alla tipologia di sito web e allo scopo di business per cui il video viene utilizzato. Per capire in quali modi sfruttare il video come strumento di marketing e analizzare il comportamento dei visitatori su di esso, è necessario fare prima un passo indietro, facendo una panoramica sui principali aspetti legati ad esso: dalla sua produzione, all'inserimento sulle pagine web, i player per farlo, e la diffusione attraverso i siti di social netwok e su tutti i nuovi dispositivi e le piattaforme connessi nella rete. A questo proposito viene affrontata la panoramica generale di approfondimento sugli aspetti più tecnici, dove vengono mostrate le differenze tra i formati di file e i formati video, le tecniche di trasmissione sul web, come ottimizzare l'inserimento dei contenuti sulle pagine, la descrizione dei più famosi player per l'upload, infine un breve sguardo sulla situazione attuale riguardo alla guerra tra formati video open source e proprietari sul web. La sezione finale è relativa alla parte più pratica e sperimentale del lavoro. Nel capitolo 7 verranno descritte le principali funzionalità di due piattaforme di web analytics tra le più utilizzate, una gratuita, Google Analytics e una a pagamento, Omniture SyteCatalyst, con particolare attenzione alle metriche per il tracciamento video, e le differenze tra i due prodotti. Inoltre, mi è sembrato interessante illustrare le caratteristiche di alcune piattaforme specifiche per la video analytics, analizzando le più interessanti funzionalità offerte, anche se non ho avuto modo di testare il loro funzionamento nella pratica. Nell'ultimo capitolo vengono illustrate alcune applicazioni pratiche della video analytics, che ho avuto modo di osservare durante il periodo di tirocinio e tesi in azienda. Vengono descritte in particolare le problematiche riscontrate con i prodotti utilizzati per il tracciamento, le soluzioni proposte e le questioni che ancora restano irrisolte in questo campo.
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Höferlin, Markus Johannes [Verfasser], and Daniel [Akademischer Betreuer] Weiskopf. "Video visual analytics / Markus Johannes Höferlin. Betreuer: Daniel Weiskopf." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2013. http://d-nb.info/1037955935/34.

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Books on the topic "Videos analytics"

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Distante, Cosimo, Sebastiano Battiato, and Andrea Cavallaro, eds. Video Analytics for Audience Measurement. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12811-5.

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Shan, Caifeng, Fatih Porikli, Tao Xiang, and Shaogang Gong, eds. Video Analytics for Business Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28598-1.

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Shan, Caifeng. Video Analytics for Business Intelligence. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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El-Alfy, El-Sayed M., George Bebis, and Mengchu Zhou. Intelligent Image and Video Analytics. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003053262.

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Bai, Xiang, Yi Fang, Yangqing Jia, Meina Kan, Shiguang Shan, Chunhua Shen, Jingdong Wang, et al., eds. Video Analytics. Face and Facial Expression Recognition. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12177-8.

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Nasrollahi, Kamal, Cosimo Distante, Gang Hua, Andrea Cavallaro, Thomas B. Moeslund, Sebastiano Battiato, and Qiang Ji, eds. Video Analytics. Face and Facial Expression Recognition and Audience Measurement. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56687-0.

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1953-, Okuda Ted, ed. The Jerry Lewis films: An analytical filmography of the innovative comic. Jefferson, N.C: McFarland, 1995.

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Anzinger, Martina. Gainborough pictures reframed, or, Raising Jane Austen for 1990s film: A film-historic and film-analytical study for the 1955 films Sense and sensibility and Persuasion. Frankfurt am Main: P. Lang, 2003.

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Xiang, Tao, Caifeng Shan, and Fatih Porikli. Video Analytics for Business Intelligence. Springer, 2012.

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Video Analytics For Business Intelligence. Springer, 2012.

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Book chapters on the topic "Videos analytics"

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Dhanwal, Swapnil, Vishnu Bhaskar, and Tanya Agarwal. "Automated Censoring of Cigarettes in Videos Using Deep Learning Techniques." In Asset Analytics, 339–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3643-4_26.

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Guru, D. S., V. K. Jyothi, and Y. H. Sharath Kumar. "Features Fusion for Retrieval of Flower Videos." In Data Analytics and Learning, 221–33. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2514-4_19.

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Agrawal, Swati, and Ashish Sureka. "Copyright Infringement Detection of Music Videos on YouTube by Mining Video and Uploader Meta-data." In Big Data Analytics, 48–67. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03689-2_4.

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Manohar, N., Y. H. Sharath Kumar, G. Hemantha Kumar, and Radhika Rani. "Deep Learning Approach for Classification of Animal Videos." In Data Analytics and Learning, 421–31. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2514-4_35.

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Baluch, Farhan, and Laurent Itti. "Mining Videos for Features that Drive Attention." In Multimedia Data Mining and Analytics, 311–26. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14998-1_14.

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Singh, Neha, Onkareshwar Prasad, and T. Sujithra. "Deep Learning-Based Violence Detection from Videos." In Intelligent Data Engineering and Analytics, 323–32. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6624-7_32.

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Sunny, Alfina, and N. Manohar. "Detection, Classification and Counting of Moving Vehicles from Videos." In Data Analytics and Learning, 231–42. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6346-1_19.

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Ding, Lei, and Alper Yilmaz. "Learning Social Relations from Videos: Features, Models, and Analytics." In Human-Centered Social Media Analytics, 21–41. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05491-9_2.

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Shaliyar, Mohd, and Khurram Mustafa. "Source Authentication of Videos Shared on Social Media." In Intelligent Data Analytics, IoT, and Blockchain, 102–13. Boca Raton: Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003371380-10.

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Venkatraman, Santhi, and Puja Saha. "Multimodal Architecture for Emotion Prediction in Videos Using Ensemble Learning." In Big Data Analytics in Smart Manufacturing, 109–20. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003202776-6.

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Conference papers on the topic "Videos analytics"

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Chatar, Crispin, Suhas Suresha, Laetitia Shao, Soumya Gupta, and Indranil Roychoudhury. "Determining Rig State from Computer Vision Analytics." In SPE/IADC International Drilling Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/204086-ms.

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Abstract For years, many companies involved with drilling have searched for the ideal method to calculate the state of a drilling rig. While companies cannot agree on a standard definition of "rig state," they can agree that as we move forward in drilling optimization and with further use of remote operations and automation, that rig state calculation is mandatory in one form or the other. Internally in the service company, many methods exist for calculating rig state, but one new technology area holds promise to deliver a more efficient and cost-effective option with higher accuracy. This technology involves vision analytics. Currently, detection algorithms rely heavily on data collected by sensors installed on the rig. However, relying exclusively on sensor data is problematic because sensors are prone to failure and are expensive to maintain and install. By proposing a machine learning model that relies exclusively on videos collected on the rig floor to infer rig states, it is possible to move away from the existing methods as the industry moves to a future of high-tech rigs. Videos, in contrast to sensor data, are relatively easy to collect from small inexpensive cameras installed at strategic locations. Consequently, this paper presents machine learning pipeline that is implemented to perform rig state determination from videos captured on the rig floor of an operating rig. The pipeline can be described in two parts. Firstly, the annotation pipeline matches each frame of the video dataset to a rig state. A convolutional neural network (CNN) is used to match the time of the video with corresponding sensor data. Secondly, additional CNNs are trained, capturing both spatial and temporal information, to extract an estimation of rig state from videos. The models are trained on a dataset of 3 million frames on a cloud platform using graphics processing units (GPU). Some of the models used include a pretrained visual geometry group (VGG) network, a convolutional three-dimensional (C3D) model that used three-dimensional (3D) convolutions, and a two-stream model that uses optical flow to capture temporal information. The initial results demonstrate this pipeline to be effective in detecting rig states using computer vision analytics.
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Ly, Anna, Tingting Zhu, and Andrew Petersen. "Evaluating Storytelling Videos Using YouTube Analytics." In SIGCSE 2024: The 55th ACM Technical Symposium on Computer Science Education. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3626253.3635503.

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Qingbo Hu, Guan Wang, and Philip S. Yu. "Assessing the longevity of online videos: A new insight of a video's quality." In 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2014. http://dx.doi.org/10.1109/dsaa.2014.7058044.

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Zhang, Yi, Fan Luan, and Yu-Gang Jiang. "Smart Advertising in Videos Based on Comprehensive Content Analytics." In 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2019. http://dx.doi.org/10.1109/icmew.2019.00113.

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Tsai, Min-Han, Nalini Venkatasubramanian, and Cheng-Hsin Hsu. "Analytics-Aware Storage of Surveillance Videos: Implementation and Optimization." In 2020 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2020. http://dx.doi.org/10.1109/smartcomp50058.2020.00024.

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Barnes, Stuart J., and Weisha Wang. "Understanding Consumer Advertising via Audio Analytics of Sports Videos." In 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 2023. http://dx.doi.org/10.1109/icaiic57133.2023.10067125.

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Kadoic, Nikola, and Dijana Oreski. "Learning Analytics of YouTube Videos Linked to LMS Moodle." In 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, 2021. http://dx.doi.org/10.23919/mipro52101.2021.9597168.

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Saquib, Nazmus, Faria Huq, and Syed Arefinul Haque. "graphiti: Sketch-based Graph Analytics for Images and Videos." In CHI '22: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3491102.3501923.

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Momeni, Hamed, and Arvin Ebrahimkhanlou. "Applications of High-Dimensional Data Analytics in Structural Health Monitoring and Non-Destructive Evaluation: Thermal Videos Processing Using Tensor-Based Analysis." In ASME 2021 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/imece2021-71878.

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Abstract This study reviews existing and potential applications of high-dimensional data analytics in the fields of structural health monitoring and non-destructive evaluation. Contrary to the high potential of these methods, the implemented applications in structural health monitoring and non-destructive evaluation topics are limited. In addition, with the ever-increasing development of measurement equipment, the necessity of using these methods is enhancing. In this paper, videos captured by different non-destructive evaluation techniques are studied as an example of high-dimensional data. Thermal videos are used for automatic damage detection and localization. Particularly, thermal cameras are employed to find delamination zones in composite plates, commonly used in aircraft wings. Due to the high-dimensional intrinsic of videos, using conventional statistical methods raise theoretical and practical challenges. One of the solutions to overcome these challenges is implementing tensor-based data analysis to analyze videos. Two tensor factorization methods are presented and employed to localize the damage automatically. The results show that the recorded video can be represented by a few vectors, which easily extract the time variation and extent of the damage.
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M.R., Anala, Malika Makker, and Aakanksha Ashok. "Anomaly Detection in Surveillance Videos." In 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, 2019. http://dx.doi.org/10.1109/hipcw.2019.00031.

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Reports on the topic "Videos analytics"

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Simpson, Diane, Michelle Brennan, and Susan Gomperts. Strategic roadmap for interoperable public safety video analytics. Gaithersburg, MD: National Institute of Standards and Technology, March 2020. http://dx.doi.org/10.6028/nist.ir.8299.

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Simpson, Diane, Michelle Brennan, and Susan Gomperts. Strategic roadmap for interoperable public safety video analytics. Gaithersburg, MD: National Institute of Standards and Technology, April 2020. http://dx.doi.org/10.6028/nist.sp.1500-15.

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Guan, Haiying, Daniel Zhou, Jonathan Fiscus, John Garofolo, and James Horan. Evaluation infrastructure for the measurement of content-based video quality and video analytics performance. Gaithersburg, MD: National Institute of Standards and Technology, July 2017. http://dx.doi.org/10.6028/nist.ir.8187.

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Russell, John. Deliberate Motion Analytics Fused Radar and Video Test Results Deployed Beyond the Perimeter Fence in a High Noise Environment. Office of Scientific and Technical Information (OSTI), May 2021. http://dx.doi.org/10.2172/1855028.

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Aguilar, G., H. Waqa-Sakiti, and L. Winder. Using Predicted Locations and an Ensemble Approach to Address Sparse Data Sets for Species Distribution Modelling: Long-horned Beetles (Cerambycidae) of the Fiji Islands. Unitec ePress, December 2016. http://dx.doi.org/10.34074/book.008.

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In response to unique species in Fiji which are threatened or endangered, and in critical need of effective conservation measures to ensure their survival, author Glenn Aguilar has produced an eMedia publication and learning research tool, called GIS For Conservation.The eMedia website hosts tutorial material, videos and modelling results for conservation management and planning purposes. Users will learn spatial analytical skills, species distribution modelling and other relevant GIS tools, as well as enhance ArcMap skills and the species distribution modelling tool Maxent. Accompanying the GIS For Conservation website is a peer-reviewed research report. The report details the case study and research methods that have informed the eMedia publication, focusing on the development of maps predicting the suitability of the Fiji Islands for longhorned beetles (Cerambycidae) that include endemic and endangered species such as the Giant Fijian Beetle Xixuthrus heros.
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Pokryshen, Dmytro A., Evgeniy H. Prokofiev, and Albert A. Azaryan. Blogger and YouTube services at a distant course “Database management system Microsoft Access”. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3272.

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The article is devoted to the coverage of the course “Database management system Microsoft Access”, an educational blog review “The development of a creative child. ІCТ”, which is used as an auxiliary tool for promoting a course and teacher in the Internet, structural analysis of this blog is made. The channel location is set on YouTube video hosting and how it is used in the course on databases. Attention is drawn to the fact that theoretical and practical material is considered on real, implemented informational and analytical systems. To prepare students for the Olympiads and provide methodological help teachers of computer science are looking at tasks from databases that were offered at the All-Ukrainian Olympiads on Information Technologies, especially II, III and IV stages (offline and online Olympiads), which are located in open access to the blog and YouTube channel. The main focus of the article is devoted to the practical side of teaching teachers of computer science, experience in using the above technologies.
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Yatsymirska, Mariya. MODERN MEDIA TEXT: POLITICAL NARRATIVES, MEANINGS AND SENSES, EMOTIONAL MARKERS. Ivan Franko National University of Lviv, February 2022. http://dx.doi.org/10.30970/vjo.2022.51.11411.

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The article examines modern media texts in the field of political journalism; the role of information narratives and emotional markers in media doctrine is clarified; verbal expression of rational meanings in the articles of famous Ukrainian analysts is shown. Popular theories of emotions in the process of cognition are considered, their relationship with the author’s personality, reader psychology and gonzo journalism is shown. Since the media text, in contrast to the text, is a product of social communication, the main narrative is information with the intention of influencing public opinion. Media text implies the presence of the author as a creator of meanings. In addition, media texts have universal features: word, sound, visuality (stills, photos, videos). They are traditionally divided into radio, TV, newspaper and Internet texts. The concepts of multimedia and hypertext are related to online texts. Web combinations, especially in political journalism, have intensified the interactive branching of nonlinear texts that cannot be published in traditional media. The Internet as a medium has created the conditions for the exchange of ideas in the most emotional way. Hence Gonzo’s interest in journalism, which expresses impressions of certain events in words and epithets, regardless of their stylistic affiliation. There are many such examples on social media in connection with the events surrounding the Wagnerians, the Poroshenko case, Russia’s new aggression against Ukraine, and others. Thus, the study of new features of media text in the context of modern political narratives and emotional markers is important in media research. The article focuses review of etymology, origin and features of using lexemes “cмисл (meaning)” and “сенс (sense)” in linguistic practice of Ukrainians results in the development of meanings and functional stylistic coloring in the usage of these units. Lexemes “cмисл (meaning)” and “сенс (sense)” are used as synonyms, but there are specific fields of meanings where they cannot be interchanged: lexeme “сенс (sense)” should be used when it comes to reasonable grounds for something, lexeme “cмисл (meaning)” should be used when it comes to notion, concept, understanding. Modern political texts are most prominent in genres such as interviews with politicians, political commentaries, analytical articles by media experts and journalists, political reviews, political portraits, political talk shows, and conversations about recent events, accompanied by effective emotional narratives. Etymologically, the concept of “narrative” is associated with the Latin adjective “gnarus” – expert. Speakers, philosophers, and literary critics considered narrative an “example of the human mind.” In modern media texts it is not only “story”, “explanation”, “message techniques”, “chronological reproduction of events”, but first of all the semantic load and what subjective meanings the author voices; it is a process of logical presentation of arguments (narration). The highly professional narrator uses narration as a “method of organizing discourse” around facts and impressions, impresses with his political erudition, extraordinary intelligence and creativity. Some of the above theses are reflected in the following illustrations from the Ukrainian media: “Culture outside politics” – a pro-Russian narrative…” (MP Gabibullayeva); “The next will be Russia – in the post-Soviet space is the Arab Spring…” (journalist Vitaly Portnikov); “In Russia, only the collapse of Ukraine will be perceived as success” (Pavel Klimkin); “Our army is fighting, hiding from the leadership” (Yuri Butusov).
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