改进的mean shift目标跟踪算法

提出一种改进的均值偏移（mean shift， MS）目标跟踪算法，用于提高模板漂移和大面积遮挡情况下视觉目标跟踪算法的鲁棒性和准确性。首先判断目标是否存在遮挡现象，当遮挡未发生时，采用原始MS算法跟踪目标，利用选择性分量更新策略对目标模板进行更新，减少模板漂移的影响|当遮挡发生后，利用非对称核函数模型对候选目标模型进行矫正，降低遮挡像素点对MS矢量和目标跟踪稳定性的影响。实验结果表明，改进的跟踪算法对非刚性和大面积遮挡目标都能进行稳定的跟踪。

Improved mean shift target tracking algorithm

n improved mean shift (MS) target tracking algorithm is proposed to improve the robustness and accuracy of tracking under template drift and large area occlusion. Firstly， the method predicts whether the target is in occlusion. If the target is not in occlusion, the original MS algorithm is used to track targets and the target template update strategy based on selected component is used to reduce the influence of template drift| when the target is occluded, the target candidate model is corrected by an asymmetric kernel model to reduce the influence of occluded pixels on MS vector and target tracking stability. The experiment result shows that the proposed algorithm can steadily track non-rigid and large area occlusion targets.