Abstract
An appearance-based feature set is proposed. With Hidden Markov Model (HMM) handling any temporal variance, the contributions of features, which are from full foreground sequence and from temporal difference sequence, are compared in details by methods which are based on feature selecting and feature voting. The experimental analysis shows that the comparative contributions can be achieved for human action identifying by the two data sources. This introduces the opportunity to analyze human behavior based on temporal difference sequence instead of full foreground sequence, and validates the far-reaching significance of this work.
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