基于均值偏移和卡尔曼滤波的目标跟踪方法

分析了Mean—shift难以有效地跟踪复杂背景下灰度运动目标的主要缺陷，提出了结合Mean-shift和卡尔曼滤波器的目标跟踪方法。该方法利用卡尔曼滤波器预测目标在当前时刻的起始位置，然后Mean-shift在该位置的邻域内寻找目标所处位置。同时。采用Bhattacharyya系数度量“目标模型”和“候选模型”相似程度．确定“候选模型”是否更换为“目标模型”，避免目标模型过度更新。以地物为背景的飞机目标图像序列试验结果表明该方法较原Mean-shift方法可明显提高阻挡情况下的目标跟踪稳定性。

Target Tracking Based on Mean-shift and Kalman Filter

After analyzing the theoretic limitation of the Mean-shift to track gray target in complex background, a method, which combines Mean-shift and Kalman filter, is proposed. Firstly, the starting position of Mean-shift is predicted by Kalman filter at present, and then the Mean-shift is utilized to track the target position around the starting position. At the same time. the Bhattacharyya coefficient is adopted to measure the comparability between the target model and the candidate model, and then determines whether or not the target model is replaced by the latter to avoid the target model being updated excessively. Experiments based on the sequence gray image with ground as main background are carried out, and the results show that, with the proposed method, the tracking stability and adaptability for the gray imaging target, even in occlusion, are improved significantly.