Human action recognition with skeletal information from depth camera

Abstract

We propose a human action recognition solution from the human's skeletal information. The angular representation of the skeleton shows its invariance to the scale of the actor and the orientation to the camera, while it maintains the correlation among different body parts. A modified Dynamic Time Warping (DTW) as a template matching solution is applied to do the action classification task. We collect our data with XBOX Kinect platform, a well-known Chinese traditional shadow boxing named Taiji is recognized based on types of actions which achieves the accuracy of 80%.