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Titlebook: Contextual Analysis of Videos; Myo Thida,How-lung Eng,Paolo Remagnino Book 2013 Springer Nature Switzerland AG 2013

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發(fā)表于 2025-3-21 16:04:58 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Contextual Analysis of Videos
編輯Myo Thida,How-lung Eng,Paolo Remagnino
視頻videohttp://file.papertrans.cn/237/236909/236909.mp4
叢書名稱Synthesis Lectures on Image, Video, and Multimedia Processing
圖書封面Titlebook: Contextual Analysis of Videos;  Myo Thida,How-lung Eng,Paolo Remagnino Book 2013 Springer Nature Switzerland AG 2013
描述Video context analysis is an active and vibrant research area, which provides means for extracting, analyzing and understanding behavior of a single target and multiple targets. Over the last few decades, computer vision researchers have been working to improve the accuracy and robustness of algorithms to analyse the context of a video automatically. In general, the research work in this area can be categorized into three major topics: 1) counting number of people in the scene 2) tracking individuals in a crowd and 3) understanding behavior of a single target or multiple targets in the scene. This book focusses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene. Firstly, this book surveys the state-of-the-art methods for tracking multiple targets in a complex scene and describes the authors‘ approach for tracking multiple targets. The proposed approach is to formulate the problem of multi-target tracking as an optimization problem of finding dynamic optima (pedestrians) where these optima interact frequently. A novel particle swarm optimization (PSO) algorithm that uses a set of multiple swarms is presented. Through particles and swarms div
出版日期Book 2013
版次1
doihttps://doi.org/10.1007/978-3-031-02249-4
isbn_softcover978-3-031-01121-4
isbn_ebook978-3-031-02249-4Series ISSN 1559-8136 Series E-ISSN 1559-8144
issn_series 1559-8136
copyrightSpringer Nature Switzerland AG 2013
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沙發(fā)
發(fā)表于 2025-3-21 21:10:29 | 只看該作者
板凳
發(fā)表于 2025-3-22 00:48:17 | 只看該作者
Tracking Multiple Targets Using Particle Swarm Optimisation,andard (PSO) algorithm and its variants for tracking targets in surveillance videos. The proposed method extends the standard PSO algorithm to the problem of finding dynamic optima (pedestrians) where these optima interact frequently.
地板
發(fā)表于 2025-3-22 08:38:44 | 只看該作者
Valentí Rull,Teresa Vegas-Vilarrúbiae systems are being installed everywhere, ranging from residential areas to public spaces such as airports and shopping malls. As these surveillance systems collect a huge amount of video data everyday, it is important to automate the process of video context analysis (Figure 1.1). Automating survei
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發(fā)表于 2025-3-22 10:39:58 | 只看該作者
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發(fā)表于 2025-3-22 16:46:39 | 只看該作者
Tomasz Zurek,Jonathan Kwik,Tom van Engersng a few individuals in a complex scene and analyse their behaviour. However, as the number of people in the scene increases, the problem of tracking individual targets becomes more challenging. As a result, tracking-based approaches are inadequate for analysing the behaviours of a crowd. To address
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發(fā)表于 2025-3-22 18:23:13 | 只看該作者
https://doi.org/10.1007/978-3-031-58202-8The work carried out in this book addressed two significant problems encountered in numerous computer vision applications: (i) the tracking multiple targets in a complex scene, and (ii) the detection and localisation of abnormal regions in crowded scenes.
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發(fā)表于 2025-3-22 21:33:11 | 只看該作者
Conclusion,The work carried out in this book addressed two significant problems encountered in numerous computer vision applications: (i) the tracking multiple targets in a complex scene, and (ii) the detection and localisation of abnormal regions in crowded scenes.
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發(fā)表于 2025-3-23 01:57:00 | 只看該作者
Contextual Analysis of Videos978-3-031-02249-4Series ISSN 1559-8136 Series E-ISSN 1559-8144
10#
發(fā)表于 2025-3-23 07:03:26 | 只看該作者
Stefano De Giorgis,Aldo Gangemiandard (PSO) algorithm and its variants for tracking targets in surveillance videos. The proposed method extends the standard PSO algorithm to the problem of finding dynamic optima (pedestrians) where these optima interact frequently.
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