改进权值计算的均值移动目标跟踪

针对基于Bhattacharyya相似度的均值移动跟踪算法精度较差的问题，提出一种基于直方图交集思想的新型颜色分量加权方法，该方法利用参考模板与候选模板归一化颜色概率密度对应颜色分量的比值作为均值移动算法的加权系数。新权值计算方法在目标快速运动,有场景相似颜色干扰等情况下具有很好的适用能力，从而提高目标的跟踪精度。另外处理跟踪过程中，因摄像机抖动、光照变化等因素导致跟踪线索变化的情况，利用基于辅助模板的目标更新机制，有效地解决了目标短暂遮挡以及更新过程中的累积误差问题。通过多组对比实验结果可以看出，算法具有更强地抑制背景干扰以及特征自适应的能力，从而提高了均值移动跟踪算法的鲁棒性。

Target tracking based on mean shift with improved weights

Aimed to solve the poor tracking accuracy problem of mean shift based on Bhattacharyya similarity, a novel weighting method is proposed based on the enhanced histogram intersection. The paper uses the ratio between the color components of the normalized color probability densities of the candidate model and that of the reference model as the weight coefficient. Results show that the new color weighting coefficient method has good adaptability under fast object moving or color similarity in scene, and the target tracking precision is enhanced effectively. In addition, a template updating method based on auxiliary model mechanism is used to solve the problems of camera vibration, illumination and object changing, therefore the cumulative error problem is also effectively alleviated. Through groups of contrast experiments, the new tracker shows its good adaptive ability and its robustness.