Recognition of Human Actions Using Motion Capture Data and Support Vector Machine

Abstract

This paper presents a human action recognition system based on motion capture features and support vector machine (SVM). We use 43 optical markers distributing on body and extremities to track the movement of human actions. In our system 21 different types of action are recognized. Applying SVM for the recognition of human action the overall prediction accuracy achieves to 84.1% when using the three-fold cross validation on the training set. Another purpose of this study is to find out which skeleton points are important for human action recognition. The experimental results show that the skeleton points of head, hands and feet are the most important features for recognition of human actions.