KEYWORDS: Principal component analysis, Video, Analytical research, Data modeling, Video surveillance, Machine learning, Electrical engineering, 3D image processing, Binary data, Facial recognition systems
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.
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