基于MeanShift算法的运动人体跟踪

用于运动目标跟踪的MeanShift算法主要是通过单一直方图描述目标颜色特征来实现的，它明显缺少有关空间分布方面的信息。针对该缺陷，Maggio E等人提出了使用目标区域分块的改进方法，但在复杂环境下判别效果和稳定性不够好。为此，本文提出了新的改进方法。一方面通过减少人体区域分块数目来减少处理时间，但又不失相关空间信息
；另一方面通过对每个分块进行一定系数的加权来提高判别效果。实验比较证明，该算法提高了复杂环境下运动人体判别的准确性，具有很好的稳定性。

Tracking Moving People Based on the MeanShift Algorithm

The MeanShift algorithm which is applied to tracking moving objects mainly uses a single histogram to describe the color characteristics of the object.This method obviously lacks spatial distribution information.As for this defect,Emilio Maggio et al have put forward an improved algorithm of blocking the object into regions.But the discriminant effect and the stability are not good enough in a complex environment.So this paper proposes a new method to improve it.On the one hand,reducing the number of human body regions is used to cut down the processing time without losing the space-related information.On the other hand,each sub-block is weighted by certain coefficients so as to improve the discriminant effect.The comparison experiment proves that the algorithm promotes the accuracy of identifying the moving people under a complicated environment and it has good stability.