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Proceedings Paper

Human activity recognition in video using two methods for matching shape contexts of silhouettes
Author(s): Natasha Kholgade; Andreas Savakis
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Paper Abstract

In this paper, activity recognition is performed based on silhouettes of the human figure obtained by background subtraction and characterized by the shape context, a log-polar histogram derived from boundary points. In the first approach each video frame is tagged by the activity corresponding to the closest matches between the query and known shapes. In the second method, the shape context dimensionality is reduced by principal components analysis, and a neural network is used for activity classification of individual frames. The overall decision for an entire video sequence is based on majority vote. Classification of individual frames ranged between 70-90% and overall classification of video sequences was very accurate.

Paper Details

Date Published: 15 April 2008
PDF: 8 pages
Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 696108 (15 April 2008); doi: 10.1117/12.777764
Show Author Affiliations
Natasha Kholgade, Rochester Institute of Technology (United States)
Andreas Savakis, Rochester Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6961:
Intelligent Computing: Theory and Applications VI
Kevin L. Priddy; Emre Ertin, Editor(s)

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