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 analyze 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 focuses on tracking individual targets and detecting abnormal behavior of a crowd in a complex scene

    2. Literature review -- 2.1 Overview -- 2.2 Tracking multiple targets -- 2.2.1 Tracking multiple targets using particle filter -- 2.2.2 Tracking multiple targets using additional cues -- 2.2.3 Multiple-camera tracking -- 2.3 Analysis of crowd behaviour -- 2.3.1 Abnormality detection using micro-observation -- 2.3.2 Abnormality detection using macro-observation -- 2.3.3 Event detection -- 2.3.4 Graph-based and manifold learning algorithms -- 2.4 Summary --

    1. Introduction -- 1.1 Aims and objectives -- 1.2 Challenges -- 1.3 Nomenclature -- 1.4 Contributions -- 1.5 Organisation --

    3. Tracking multiple targets using particle swarm optimisation -- 3.1 Introduction -- 3.2 Literature review on particle swarm optimisation -- 3.3 Standard particle swarm optimisation -- 3.3.1 Convergence criteria -- 3.3.2 Pseudo-code -- 3.4 A modified PSO with interactive swarms -- 3.4.1 Particle and swarm diversification -- 3.4.2 Swarm optimisation -- 3.4.3 Swarm initialisation and termination -- 3.4.4 Algorithm summary -- 3.5 Experiments -- 3.5.1 Tracking fixed and known number of targets -- 3.5.2 Tracking unknown and varying number of targets -- 3.5.3 Performance evaluation -- 3.6 Summary --

    4. Abnormality detection in crowded scenes -- 4.1 Introduction -- 4.2 Global abnormality detection -- 4.2.1 Frame-based video representation -- 4.2.2 Spatio-temporal Laplacian Eigenmaps -- 4.2.3 Analysing video manifolds in temporal domain -- 4.2.4 Experimental results -- 4.3 Local abnormality detection -- 4.3.1 Representation of local motion -- 4.3.2 Temporally constrained Laplacian Eigenmaps -- 4.3.3 Representation of regular motion pattern -- 4.3.4 Abnormality detection -- 4.3.5 Abnormality localisation -- 4.3.6 Experimental results -- 4.4 Summary --

    5. Conclusion -- 5.1 Future directions -- Bibliography -- Authors' biographies


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    Titel :

    Contextual Analysis of Videos


    Beteiligte:
    Thida, Myo (Autor:in) / Eng, How-lung (Autor:in) / Monekosso, Dorothy (Autor:in)


    Erscheinungsdatum :

    2013


    Format / Umfang :

    1 Online-Ressource (104 Seiten)


    Anmerkungen:

    Campusweiter Zugriff (Universität Hannover) - Vervielfältigungen (z.B. Kopien, Downloads) sind nur von einzelnen Kapiteln oder Seiten und nur zum eigenen wissenschaftlichen Gebrauch erlaubt. Keine Weitergabe an Dritte. Kein systematisches Downloaden durch Robots.
    Description based upon print version of record




    Medientyp :

    Buch


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    006.3 / 006.37



    General Videos

    Online Contents | 2001


    Videos Received

    Online Contents | 1994


    Offers - Videos

    Online Contents | 2001


    Offers - Videos

    Online Contents | 2001


    Contextual outline

    Reiche, Sascha | Springer Verlag | 2017