Abstract. During object tracking, Fast tracking via Spatio-Temporal Context Learning which combines temporal correlation among sequential frames and spatial correlation between object and background can solve the problem of semi-occlusion, but not full-occlusion. Kalman Filter makes use of the predictive value and measurement to calculate the optimal state. This paper aims at solving the full-occlusion problems by combining the algorithm and Kalman Filter together. Experiments show that the improved STC can solve occlusion problems effectively.