An adaptive framework for silhouette based human action recognition

Understanding the actions of humans from videos is a demanding task in Computer Vision. Identifying the actions being accomplished by the human in the video sequence automatically and tagging their actions is the prime functionality of intelligent video systems. In general the major issues of Human Action Recognition are dynamic backgrounds, camouflage, occlusion handling, similar action recognition, incremental learning etc. Among these issues the major hurdle is to differentiate the similar actions and incremental learning. To resolve the above aforementioned issues, an adaptive framework for silhouette based Human Action Recognition is proposed. Here the silhouettes are detected by combining Adaptive Guassian Mixture Model and Contour Saliency. Similar Action Handling is done by integrating Bipartite matching with the class certainty measure. The new actions are identified by invoking adaptation method thus making the system adaptable to dynamic conditions. The action labels are identified using Support Vector Machine - Nearest Neighbor classifier. Experimental results show the higher recognition rate achieved for various similar actions with the benchmarking datasets such as Weizmann, UCF-Sports Action dataset.