Human activity recognition in video using two methods for matching shape contexts of silhouettes

Natasha Kholgade; Andreas Savakis

doi:10.1117/12.777764

15 April 2008 Human activity recognition in video using two methods for matching shape contexts of silhouettes

Natasha Kholgade, Andreas Savakis

Proceedings Volume 6961, Intelligent Computing: Theory and Applications VI; 696108 (2008) https://doi.org/10.1117/12.777764
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

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.

Citation Download Citation

Natasha Kholgade and Andreas Savakis "Human activity recognition in video using two methods for matching shape contexts of silhouettes", Proc. SPIE 6961, Intelligent Computing: Theory and Applications VI, 696108 (15 April 2008); https://doi.org/10.1117/12.777764

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