A mean shift (MS) object tracking algorithm with a corrected background-weighted histogram(CBWH) only provides CBWH update but lacks an object template update. Moreover

it exhibits poor robustness in case of object occlusion.Our algorithm combines the reliability of the Kalman filter (KF) in terms of object state prediction and parameter updating

and applies two layers of the KF framework into MS with CBWH. The first layer of the KF framework for predicting object location achieves adaptive tracking results by applying the relationship between KF noise and the Bhattacharyya coefficient

and thus

reduces occlusion effect on the tracking results. The second layer of the KF framework for updating the object template achieves update synchronization of the object template and CBWH by filtering each nonzero element in the object template

and consequently

reduces the effect of changes in object features on the tracking results. Under background interference

occlusion

and characteristic change

the average tracking errors of our algorithm

MS with CBWH

and traditional MS are 5.43

19.2

and 51.43

respectively. This result shows that the tracking precision of our algorithm is the highest. Our algorithm also performs well in real time. Our algorithm adds two layers of the KF framework into MS with CBWH

thereby solving the weakness of the initial algorithm

which does not provide a template update and exhibits poor robustness in case of object occlusion.The effectiveness of our algorithm is verified in the experiments.