Human motion recognition using three-dimensional skeleton model based on RGBD vision system

Abstract

Human action recognition is one of the most important topics in computer vision. It has a lot of applications in intelligent monitoring, human-computer interaction, virtual reality, motion analysis and video annotation, etc. However, the effects of recognition are usually affected by human occlusion, self-occlusion, ambiguity and so on. In view of the above problems, this article describes an algorithm to recognize human motion using three-dimensional skeleton model based on RGBD vision system. Firstly, the human body three-dimensional skeleton model is established with the Kinect. Next, the normalized relative positions of skeleton joints and joint angles are adopted as features, and the action database containing the color image, the depth image, the skeletal image and the coordinates of the joints is built. Then, the features are trained and classified by multi-class support vector machine classifier. Finally, human motion recognitions are carried out. Experimental results indicate that the algorithm can recognize 12 kinds of actions behaved by different humans effectively.