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Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition
conference contribution
posted on 2008-12-01, 00:00 authored by Sunil GuptaSunil Gupta, Y S Kumar, K R RamakrishnanWe 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. © 2008 IEEE.
History
Event
Computer Vision, Graphics and Image Processing. Indian Conference (6th : 2008 : Bhubaneswar, India)Pagination
111 - 118Publisher
IEEELocation
Bhubaneswar, IndiaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2008-12-16End date
2008-12-19ISBN-13
9780769534763Language
engPublication classification
E Conference publication; E1.1 Full written paper - refereedCopyright notice
2008, IEEETitle of proceedings
ICVGIP 2008 : Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image ProcessingUsage metrics
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