Activity recognition from video using layered approach

Charles A. McPherson; John M. Irvine; Mon Young; Anthony Stefanidis

doi:10.1117/12.909585

23 January 2012 Activity recognition from video using layered approach

Charles A. McPherson, John M. Irvine, Mon Young, Anthony Stefanidis

Proceedings Volume 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques; 83010S (2012) https://doi.org/10.1117/12.909585
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

The adversary in current threat situations can no longer be identified by what they are, but by what they are doing. This has lead to a large increase in the use of video surveillance systems for security and defense applications. With the quantity of video surveillance at the disposal of organizations responsible for protecting military and civilian lives comes issues regarding the storage and screening the data for events and activities of interest. Activity recognition from video for such applications seeks to develop automated screening of video based upon the recognition of activities of interest rather than merely the presence of specific persons or vehicle classes developed for the Cold War problem of "Find the T72 Tank". This paper explores numerous approaches to activity recognition, all of which examine heuristic, semantic, and syntactic methods based upon tokens derived from the video. The proposed architecture discussed herein uses a multi-level approach that divides the problem into three or more tiers of recognition, each employing different techniques according to their appropriateness to strengths at each tier using heuristics, syntactic recognition, and HMM's of token strings to form higher level interpretations.

Citation Download Citation

Charles A. McPherson, John M. Irvine, Mon Young, and Anthony Stefanidis "Activity recognition from video using layered approach", Proc. SPIE 8301, Intelligent Robots and Computer Vision XXIX: Algorithms and Techniques, 83010S (23 January 2012); https://doi.org/10.1117/12.909585

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