Adaptive Object Tracking with Online Statistical Model Update

In this paper, we propose a statistical model-based contour tracking algorithm based on the Condensation framework. The models include a novel object shape prediction model and two statistical object models. The object models consist of the grayscale histogram and contour shape PCA models computed from the previous tracking results. With the incremental singular value decomposition (SVD) technique, these three models are learned and updated very efficiently during tracking. We show that the proposed shape prediction model outperforms the affine predictor through experiments. Experimental results show that the proposed contour tracking algorithm is very stable in tracking human heads on real videos with object scaling, rotation, partial occlusion, and illumination changes.