Abstract
Recent studies have shown that exploring features of the skeleton data is vital for human action recognition. Nevertheless, how to effectively extract discriminative features is still a challenging work. In this paper, we propose a novel method that extracts a sine feature for human action recognition from skeleton data. Kinect is used to extract human skeleton information (3D coordinates of each joint point) firstly, then two joint points are connected to define a skeleton vector according to the principle of human body structure, and the sine feature of each skeleton vector is calculated as a new pose description feature, SVM is used to classify the obtained features for human action classification. Experimental results on Cornell Activity Database are provided, and the results demonstrate the effectiveness of our approach.
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