Struck is a vision target tracking algorithm with better comprehensive performance in recent year, but for larger proportion of occlusion or all occlusion, the performance of the algorithm decreases obviously. The analysis of Struck shows that the classifier will learn wrong information when occlusion occurs which result in tracking failure or tracking drifting. Accordingly, an active occlusion detection method is applied to Struck framework in this paper, the classifier update is stopped after large proportion of occlusion is detected and zooming the search samples to reduce the false alarm of occlusion detection caused by size change of the object; as for the object with available motion information, utilize Kalman filter to achieve continuous tracking after total occlusion. The experiment demonstrates that the proposed algorithm framework is of high robustness for object tracking under occlusion.