Modeling human actions from learning

Human action understanding is crucial to the success of many human-machine interfaces based on vision. In this research, we apply artificial intelligence and statistical techniques towards observation of people, leading to modeling of their actions, and understanding of their intentions. In order to actualize the paradigm of learning from demonstration, a tracking system that is capable of locating the head and hand positions of moving humans has been developed. We propose to classify the motion trajectories of humans in the scene by using support vector classification. Since the data size of human motion trajectories is large, we apply principal component analysis (PCA) and independent component analysis (ICA) for data reduction. We have successfully applied the developed technique on two different applications: action recognition of table tennis players, and detection of human fighting motions.