Part-based motion descriptor image for human action recognition
Pattern Recognition
Ongoing human action recognition with motion capture
Pattern Recognition
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This paper proposes a novel human action recognition approach which represents each video sequence by a cumulative skeletonized images (called CSI) in one action cycle. Normalized-polar histogram corresponding to each CSI is computed. That is the number of pixels in CSI which is located in the certain distance and angles of the normalized circle. Using hierarchical classification in two levels, human action is recognized. In first level, course classification is performed with whole bins of histogram. In the second level, the more similar actions are examined again employing the special bins and the fine classification is completed. We use linear multi-class SVM as the classifier in two steps. Real human action dataset, Weizmann, is selected for evaluation. The resulting average recognition rate of the proposed method is 97.6%.