A framework for a video analysis tool for suspicious event detection

Authors:
Gal Lavee;Latifur Khan;Bhavani Thuraisingham
Affiliations:
The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas;The University of Texas at Dallas, Richardson, Texas
Venue:
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
Year:
2005

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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 diction technique is presented and future an 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.