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Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition

Gupta, Sunil Kumar, Kumar, Y. Senthil and Ramakrishnan, K.R. 2008, Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition, in ICVGIP 2008 : Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image Processing, IEEE, Piscataway, N.J., pp. 111-118, doi: 10.1109/ICVGIP.2008.58.

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Title Learning feature trajectories using Gabor Filter Bank for human activity segmentation and recognition
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Kumar, Y. Senthil
Ramakrishnan, K.R.
Conference name Computer Vision, Graphics and Image Processing. Indian Conference (6th : 2008 : Bhubaneswar, India)
Conference location Bhubaneswar, India
Conference dates 16-19 Dec. 2008
Title of proceedings ICVGIP 2008 : Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image Processing
Publication date 2008
Start page 111
End page 118
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Summary 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. © 2008 IEEE.
ISBN 9780769534763
Language eng
DOI 10.1109/ICVGIP.2008.58
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082931

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
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