Due to the changes of target and background during tracking

traditional single classifier tracking algorithms learn a lot of non-target information which result in the decrease of tracking accuracy.In this paper

we propose to use tree structure to save former classifiers as a set.In each frame

a subset of classifiers are chosen according to the path in the tree to classify test samples.We propose a classification algorithm based on cluster similarity comparison.A normalized feature space is established according to the variance of the cluster.The target in a new frame could be got by computing the distance between test samples and the center of the cluster.Experiments show that our algorithm could achieve the goal of robust tracking under complicated conditions.