目的 考虑到融合校正背景权重直方图(CBWH)的Mean Shift(MS)目标跟踪算法只有CBWH更新而缺少目标模板更新,以及在目标遮挡时鲁棒性欠佳的不足。方法 结合卡尔曼滤波器(KF)在目标状态预测和参数更新方面的可靠性,将两层KF框架融入融合CBWH的MS。第1层KF框架为目标位置预测层,通过KF噪声与巴氏系数之间的关系,实现跟踪结果的自适应调整,减少遮挡对跟踪结果的影响;第2层KF框架为目标模板更新层,通过KF对目标模板中的每个非零元素进行滤波,实现目标模板与CBWH的同步更新,减少目标特征变化对跟踪结果的影响。结果 在背景干扰、遮挡以及特征变化等条件下进行实验,得到本文算法、融合CBWH的MS和传统MS的平均跟踪误差分别为5.43、19.2和51.43,显示本文算法的跟踪精度最高。同时本文算法也具有良好的实时性。结论 本文算法在融合CBWH的MS基础上,加入两层KF框架,解决了原算法缺少目标模板更新和在目标遮挡时鲁棒性欠佳的不足,最后实验验证了本文算法的有效性。

Objective 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.Method 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. Result 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. Conclusion 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.