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Video Analysis Based on Volumetric Event Detection

Wang, Jing and Xu, Zhijie (2010) Video Analysis Based on Volumetric Event Detection. International Journal of Automation and Computing, 7 (3). pp. 365-371. ISSN 1476-8186

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    Abstract

    During the past decade, the feature extraction and the knowledge acquisition based on video analysis have been extensively researched and tested on many applications such as CCTV data analysis, large-scale public event control, and other daily security monitoring and surveillance operations with various degrees of success. However, since the actual video process is a multi-phased one and encompasses extensive theories and techniques ranging from fundamental image processing, computational geometry and graphics, machine vision, to advanced artificial intelligence, pattern analysis, and even cognitive science, there are still many important problems to resolve before it can be widely applied. Among them, the video event identification and detection are the two prominent ones. Comparing with the most popular frame-to-frame processing mode of most of today’s approaches and systems, this project reorganizes the video data as a 3D volume structure which provides the hybrid spatial and temporal information in a unified space. This paper reports an innovative technique to transform original video frames to 3D volume structures denoted by spatial and temporal features. It then moves on to highlight the volume array structure in a so called “pre-suspicion” mechanism for later process. The focus of this report is the development of an effective and efficient voxel based segmentation technique suitable to the volumetric nature of video events and is ready for deployment in 3D clustering operations. The paper concludes at the performance evaluation of the devised technique with further discussions on the future work for accelerating the pre processing of the original video data.

    Item Type: Article
    Subjects: T Technology > T Technology (General)
    Schools: School of Computing and Engineering
    School of Computing and Engineering > Computer Graphics, Imaging and Vision Research Group
    Related URLs:
    Depositing User: Cherry Edmunds
    Date Deposited: 15 Jul 2010 14:48
    Last Modified: 05 Jan 2011 12:41
    URI: http://eprints.hud.ac.uk/id/eprint/8142

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