Subspace learning for silhouette based human action recognition

Ling Shao; Rui Jin

doi:10.1117/12.862856

4 August 2010 Subspace learning for silhouette based human action recognition

Ling Shao, Rui Jin

Proceedings Volume 7744, Visual Communications and Image Processing 2010; 77441S (2010) https://doi.org/10.1117/12.862856
Event: Visual Communications and Image Processing 2010, 2010, Huangshan, China

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.

Citation Download Citation

Ling Shao and Rui Jin "Subspace learning for silhouette based human action recognition", Proc. SPIE 7744, Visual Communications and Image Processing 2010, 77441S (4 August 2010); https://doi.org/10.1117/12.862856

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