A gesture recognition interface with upper body model-based pose tracking

This paper presents a gesture recognition interface with the observed pose sequence determined by our upper body model-based pose tracking. For last decade many researchers have focused on how well tracks human poses based on predefined pose model. Then we move this discussion to the gesture recognition by pose tracking. Our system consists of two parts: pose tracking and gesture recognition. In the first part, Particle filtering is used for tracking the upper body pose with the key pose library where we try to find key pose for the proposal distribution. The particles generated from the proposal distribution with random noise, could cover the pose space which is not covered by the key pose library. In second part, the observed pose is labeled with a pose number among all key poses by comparing between key poses. HMM is used to determine the probabilities of the gesture states from the observed pose sequence. HMM parameters like transition and emission matrix are trained by the analysis on the gesture database. The experimental results shows how well gesture recognition works based on our system.