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A framework for a video analysis tool for suspicious event detection

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Abstract

This paper proposes a framework to aid video analysts in detecting suspicious activity within the tremendous amounts of video data that exists in today’s world of omnipresent surveillance video. Ideas and techniques for closing the semantic gap between low-level machine readable features of video data and high-level events seen by a human observer are discussed. An evaluation of the event classification and detection technique is presented and a future experiment to refine this technique is proposed. These experiments are used as a lead to a discussion on the most optimal machine learning algorithm to learn the event representation scheme proposed in this paper.

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Authors and Affiliations

  1. Computer Science Department, Technion — Israel Institute of Technology, Technion City, Haifa, 32000, Israel

    Gal Lavee

  2. The University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080, USA

    Latifur Khan & Bhavani Thuraisingham

Authors
  1. Gal Lavee
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  2. Latifur Khan
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  3. Bhavani Thuraisingham
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Correspondence to Gal Lavee.

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Lavee, G., Khan, L. & Thuraisingham, B. A framework for a video analysis tool for suspicious event detection. Multimed Tools Appl 35, 109–123 (2007). https://doi.org/10.1007/s11042-007-0117-8

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  • DOI: https://doi.org/10.1007/s11042-007-0117-8

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