关键词: 自适应跟踪, Mean shift, 模糊判决方法, 卡尔曼滤波

Abstract: Mean shift algorithm of moving target tracking can not always adapt to the change of the target scale because it works with an invariable bandwidth kernel during the real tracking scenes. An adaptive moving target tracking algorithm based on fuzzy inference system is proposed in this paper. The location of target in the present frame is predicted by Kalman filter. The target scale is on-line adjusted adaptively by the fuzzy inference system where log likelihood ratio for object versus background and the similarity between candidate target and model is as its input. The target is tracked by Mean shift iterations. A model updating mechanism by the similarity which combines with the log likelihood ratio is designed to update the model of the tracking target adaptively. The algorithm is robust and tracks accurately. The experimental results justify that the algorithm can adapt to the change of the target scale and the background, and the tracking precision is superior to traditional Mean shift algorithm.

Key words: adaptive tracking, Mean shift algorithm, fuzzy inference system, Kalman filter