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Proceedings Paper

Subspace learning for silhouette based human action recognition
Author(s): Ling Shao; Rui Jin
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Paper Abstract

This paper exploits different subspace learning methods applied on silhouette based action recognition and evaluates their performance. Our recognition scheme is formed by segmenting action sequence into overlapped sub-clips and using sub-models for action matching. This sub-model matching method shows advantages in processing periodic actions. The experimental results prove that human action silhouettes are very informative for action recognition and subspace analysis can effectively preserve the intrinsic structure of raw data from 3D silhouettes. The subspace learning methods compared in this paper include traditional methods - Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and recently reported Orthogonal Local Preserving Projection (OLPP). PCA is observed to perform the best regarding both accuracy and efficiency. We believe our work is helpful for further research in silhouette based action recognition combined with subspace learning methods.

Paper Details

Date Published: 4 August 2010
PDF: 8 pages
Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77441S (4 August 2010); doi: 10.1117/12.862856
Show Author Affiliations
Ling Shao, The Univ. of Sheffield (United Kingdom)
Shenzhen Institute of Advanced Integration Technology (China)
Rui Jin, Eindhoven Univ. of Technology (Netherlands)

Published in SPIE Proceedings Vol. 7744:
Visual Communications and Image Processing 2010
Pascal Frossard; Houqiang Li; Feng Wu; Bernd Girod; Shipeng Li; Guo Wei, Editor(s)

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