Abstract
Human action analysis has recently received growing interest from vision researchers. Since it is often insufficient for a single type of feature derived from action videos to characterize variations among different motions, two complementary types of features are combined for better action representation with early fusion, namely depthmap-based features (HON4D) and skeleton-based features (Fourier Temporal Pyramid). Then, SVM is applied to classify actions. The proposed approach is tested on public benchmark dataset-MSR Action3D, and the experimental evaluations demonstrate that fusion of multiple features help to improve performance significantly, compared with any single feature type.
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