A fast and accurate motion descriptor for human action recognition applications

With the availability of the recent human skeleton extraction algorithm introduced by Shotton et al. [1], an interest for skeleton-based action recognition methods has been renewed. Despite the importance of the low-latency aspect in applications, it can be noted that the majority of recent approaches has not been evaluated in terms of computational cost. In this paper, a novel fast and accurate human action descriptor named Kinematic Spline Curves (KSC) is introduced. This descriptor is built by interpolating the kinematics of joints (position, velocity and acceleration). To overcome the anthropometric and the execution rate variability, we respectively propose the use of a skeleton normalization and a temporal normalization. For this purpose, a new temporal normalization method based on the Normalized Accumulated kinetic Energy (NAE) of the human skeleton is suggested. Finally, the classification step is performed using a linear Support Vector Machine (SVM). Experimental results on challenging benchmarks show the efficiency of our approach in terms of recognition accuracy and computational latency.