基于块的Mean-shift跟踪算法

针对传统Mean-shift跟踪算法在目标发生遮挡和形态变化时跟踪性能下降的缺点，提出了一种基于块的Mean-shift跟踪算法，该算法主要特点有：(1)将跟踪目标平均分块，每小块独立进行传统Mean-shift跟踪，利用小块跟踪未被遮挡的目标部分；(2)跟踪检测器检测目标小块跟踪的有效性，筛选出无效跟踪的目标小块，解决了目标分块造成跟踪性能下降的问题；(3)归一化互相关检测器和邻域一致检测增加了对目标空间信息的检测，弥补了Mean-shift算法的局限性，增加了跟踪的鲁棒性。实验表明，该算法在目标发生遮挡和形态变化时仍然可以有效的实现跟踪。

Mean-shift Tracking Algorithm Based Block

The mean-shift tracking algorithm based on block is proposed to solve the problem that tracking performance of traditional mean-shift algorithm decline under the occlusion and variation of target. Firstly, the target is divided into some similarly sized blocks which are tracked unobstructed part of target by traditional Mean-shift algorithm. Secondly, the effectiveness of target blocks are estimated by tracking detector and the invalid blocks are screened from the target, so the problem that the reduction of tracking performance which is caused by target blocks is solved. Thirdly, much more spatial information can be detected by normalized cross correlation detector and neighborhood homogeneity detector, so the limitation of mean-shift algorithm is made up and the robustness of tracking is increased. Experiment results indicate that the algorithm is proposed can track under the occlusion and variation of target efficiently and accurately.