Skeleton-based Human Action Recognition: A Learning Method based on Active Joints

摘要

A novel method for human action recognition from the sequence of skeletal data is presented in this paper. The proposed method is based on the idea that some of body joints are inactive and do not have any physical meaning during performing an action. In other words, regardless of the subjects that perform an action, for each action only a certain set of joints are meaningfully involved. Consequently, extracting features from inactive joints is a time-consuming task. To cope with this problem, in this paper, only the dynamic of active joints is modeled. To consider the local temporal information, a sliding window is used to divide the trajectory of active joints into some consecutive windows. Feature extraction is then applied on all windows of active joints' trajectories and then by using the K-means clustering all features are quantized. Since each action has its own active joints, in this paper one-vs-all classification strategy is exploited. Finally, to take into account the global motion information, the consecutive quantized features of the samples of an action are fed into the hidden Markov model (HMM) of that action. The experimental results show that using active joints can get 96% of maximum reachable accuracy from using all joints.