Skeleton-Based Human Action Recognition by Pose Specificity and Weighted Voting

This paper introduces a human action recognition method based on skeletal data captured by Kinect or other depth sensors. After a series of pre-processing, action features such as position, velocity, and acceleration have been extracted from each frame to capture both dynamic and static information of human motion, which can make full use of the human skeletal data. The most challenging problem in skeleton-based human action recognition is the large variability within and across subjects. To handle this problem, we propose to divide human poses into two major categories: the discriminating pose and the common pose. A pose specificity metric has been proposed to quantify the discriminative level of different poses. Finally, the action recognition is actualized by a weighted voting method. This method uses the k nearest neighbors found from the training dataset for voting and uses the pose specificity as the weight of a ballot. Experiments on two benchmark datasets have been carried out, the results have illustrated that the proposed method outperforms the state-of-the-art methods.