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Abnormal activity detection in video sequences using learnt probability densities

Rao, Supriya and Sastry, PS (2003) Abnormal activity detection in video sequences using learnt probability densities. In: Conference on Convergent Technologies for Asia-Pacific Region TENCON 2003, 15-17 October, Bangalore,India, Vol.1, 369 -372.


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Video surveillance is concerned with identifying abnormal or unusual activity at a scene. In this paper, we develop stochastic models to characterize the normal activities in a scene. Given video sequences of normal activity, probabilistic models are learnt to describe the normal motion in the scene. For any new video sequences, motion trajectories are extracted and evaluated using these learnt probabilistic models to identify if they are abnormal or not. In this paper, we have employed the commonly used prototype based representation to describe the movement of individual objects. The model parameters are estimated in the maximum-likelihood framework.

Item Type: Conference Paper
Publisher: IEEE
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 Electrical Sciences > Electrical Engineering
Date Deposited: 05 Jan 2006
Last Modified: 19 Sep 2010 04:22
URI: http://eprints.iisc.ac.in/id/eprint/4848

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