Extraction of Discriminative Patterns from Skeleton Sequences for Human Action Recognition

Emergence of novel techniques, such as the invention of MS Kinect, enables reliable extraction of human skeletons from action videos. Taking skeleton data as inputs, we propose an approach in this paper to extract the discriminative patterns for efficient human action recognition. Each action is considered to consist of a series of unit actions, each of which is represented by a pattern. Given a skeleton sequence, we first automatically extract the key-frames for unit actions, and then label them as different patterns. We further use a statistical metric to evaluate the discriminative capability of each pattern, and define the bag of reliable patterns as local features for action recognition. Experimental results show that the extracted local descriptors could provide very high accuracy in the action recognition, which demonstrate the efficiency of our method in extracting discriminative patterns.

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