本发明提供了一种基于背景加权的Mean Shift(HRBW Mean Shift)算法和Kalman预测融合的遮挡跟踪方法，针对均值漂移算法中的Bhattacharyya系数受初始目标框中背景像素的影响而无法准确的判断目标的遮挡状态，均值漂移算法在遮挡判定前失效，导致Kalman预测滤波算法不能被启用或启用后不能准确地预估目标的轨迹的问题。本发明提供了一种通过在目标直方图中加入由目标直方图和背景直方图的对数似然比计算得到背景加权因子，来改进目标模型的方法，从而进一步优化Bhattacharyya系数值，使得Bhattacharyya系数法能够更准确地检测目标的遮挡状态。本发明增大了正常跟踪状态下与遮挡状态下Bhattacharyya系数的差值，有利于遮挡状态的判断，通过提高遮挡判断准确性的方式来提高遮挡时的跟踪性能。
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