Online human action recognition based on improved dynamic time warping

Online human action recognition has broad application prospect in many fields of computer vision. Simultaneously, with the advent of depth camera, it brings on a new trend of online human action recognition but still present some unique challenges. In this paper, to solve the lower accuracy of the existing online human action recognition algorithm based on depth camera, we adopt the improved Dynamic Time Warping (DTW) algorithm to reduce the pathologic alignment caused by traditional DTW algorithm in features extraction and template generation, and then the recognition accuracy will be enhanced. Finally, this proposed approach will be evaluated on MSRC-12 Kinect Gesture dataset. The experimental evaluations show that the proposed approach achieves superior performance to the state of the art algorithms.