Mean-Shift跟踪算法中目标模型的自适应更新

针对Mean—shift跟踪算法中的模型更新问题，提出利用目标历史模型和当前匹配位置处得到的观测模型，对目标核函数直方图进行Kalman滤波，从而对目标模型进行及时更新。在滤波过程中，通过分析滤波残差动态，调整滤波方程中的各种参数。Bhattacharyya系数被用作模型更新的准则。该系统能够有效地处理遮挡、光照变化等干扰，避免了模型的过更新。大量视频序列测试的结果表明，在场景遮挡、光照变化等因素的影响下，算法能够对目标外观以及尺度的变化进行稳健、实时和有效的跟踪。

Mean-Shift Tracking with Adaptive Model Update Mechanism

Considering the issue of model update within the Mean-shift framework, this paper proposes a model update method by using Kalman filters to estimate the object kernel-histogram from the previous and current object models. Filtering residuals are employed to adaptively tune all the parameters of Kalman filters. In addition, Bhattacharyya coefficient is used as a criterion for model update. Therefore, the tracker can avoid over-update. Experimental results show that the method keeps up with the object appearance and scale changes, and is robust under the influence of occlusion and illumination factors, etc.