Compared with most traditional tracking methods, the method of tracking via spatialtemporal context (STC) has significant advantage in real time performance. Extensive experimental results show that STC presents degradation of tracking accuracy caused by deformation and occlusion. To address this problem, an improved method adopting occlusiondetection strategy and local binary pattern (LBP) to replace the gray feature was proposed. When the occlusion is detected by the tracker, the updates of classifier’s parameters are stopped. For the object of linear motion, the Kalman filter was adopted to predict its location, which will address the problem when the object is occluded. Experimental results indicate that the proposed method improves the tracking accuracy, and it shows high robustness for the occluded objects.