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Compressed Domain Action Classification Using HMM

Babu, Venkatesh R and Anantharaman, B and Ramakrishnan, KR and Srinivasan, SH (2001) Compressed Domain Action Classification Using HMM. In: IEEE Workshop on Content-Based Access of Image and Video Libraries, 2001. (CBAIVL 2001), 14 December, Kauai, Hawaii, pp. 44-49.

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Abstract

This paper proposes three techniques for person independent action classification in compressed MPEG video. The features used are based on motion vectors, obtained by partial decoding of the MPEG video. The features proposed are projected ID, 2D polar and 2D Cartesian. The feature vectors are fed to Hidden Markov Model (HMM) for classification of actions. Totally seven actions were trained with distinct HMM for classification. Recognition results of more than 90% have been achieved. This work is significant in the context of emerging MPEG-7 standard for video indexing and retrieval.

Item Type: Conference Paper
Additional Information: Copyright 1990 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Information Sciences > Supercomputer Education & Research Centre
Division of Electrical Sciences > Electrical Engineering
Date Deposited: 17 Feb 2006
Last Modified: 19 Sep 2010 04:23
URI: http://eprints.iisc.ernet.in/id/eprint/5400

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