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Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition

Gupta, Sunil Kumar and Kumar, Y Senthil and Ramakrishnan, KR (2008) Learning Feature Trajectories Using Gabor Filter Bank for Human Activity Segmentation and Recognition. In: 6th Indian Conference on Computer Vision, Graphics and Image Processing, DEC 16-19, 2008, Bhubaneswar.

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We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.

Item Type: Conference Paper
Publisher: IEEE
Additional Information: Copyright 2008 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: 24 Mar 2010 07:38
Last Modified: 19 Sep 2010 05:58
URI: http://eprints.iisc.ac.in/id/eprint/26495

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