Level set tracking with dynamical shape priors

Abstract

Dynamical shape priors are curical for level set-based non- rigid object tracking with noise, occlusions or background clutter. In this paper, we propose a level set tracking framework using dynamical shape priors to capture contours changes of an object in a periodic action sequence. The framework consists of two stages - off-line training and on-line tracking. During the off-line training stage, a graph- based dominant set clustering (DSC) method is applied to learn a shape codebook with each codeword representing a certain shape mode. Then a codeword transition matrix is learnt to characterize the temporal correlations of contours of an object. During the on-line tracking stage, we fuse the knowledge of shape priors and current observations, and adopt maximum a posteriori (MAP) estimation to predict the current shape mode. The experimental results on synthetic and real video sequences demonstrate the effectiveness of our method.