Clustering Method Evaluation for Hidden Markov Model Based Real-Time Gesture Recognition

This paper deals with the development of high performance real-time system for complex dynamic gesture recognition. The various motion features are extracted from the video frames which are used by HMM classifier. We used several clustering techniques for performance evaluation of the classifier. Our system vectorises gestures into sequential symbols both for training and testing. We found very encouraging results and the proposed method has potential application in the field of human machine interaction.