A mean shift algorithm has gained special attention in recent years due to its simplicity to enable real-time tracking.
However, the traditional mean shift tracking algorithm can fail to track target under occlusions. In this paper we propose
a novel technique which alleviates the limitation of mean shift tracking. Our algorithm employs the Kalman filter to
estimate the target dynamics information. Moreover, the proposed algorithm performs the background check process to
calculate the similarity which expresses how similar to target the background is. We then find the exact target position
combining the motion estimation by Kalman filter and the color based estimation by the mean shift algorithm based on
the similarity value. Therefore, the proposed algorithm can robustly track targets under several types of occlusion, while
the mean shift and mean shift-Kalman filter algorithms fail.