Hidden Markov Model based human activity recognition using shape and optical flow based features

Recognizing human activity is an important area of research in computer vision application. Manual monitoring of all cameras continuously for longer duration is inefficient making auto-detection of activity important. In this paper shape and optical flow features are fused together and used for human activity recognition. Features extracted are found to be efficient as concluded by ANOVA test. Hidden Markov Model are generated for each activity. System is trained and tested in various indoor and outdoor environment. The method adapted is made shape and angle invariant. Accuracy achieved using least square support vector machine classifier is 80% for all activities. Hidden Markov Model resulted in better accuracy as compared to least square support vector machine classifier with accuracy of 100.00% for walking, 100.00% for hand waving, 90% for bending, 84.61% for running and 90% for side gallop activities. 100% accuracy is achieved in recognizing activity in different angle with respect to camera.