Huang, LianghuaMa, BoShen, JianbingHe, HuiShao, LingPorikli, Fatih2021-10-131057-7149http://hdl.handle.net/1885/250786In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods.This work was supported in part by the National Natural Science Foundation of China under Grant 61472036, in part by the National Basic Research Program of China (973 Program) under Grant 2013CB328805, and in part by the Australian Research Council’s Discovery Projects Funding Scheme under Grant DP150104645. Specialized Fund for Joint Building Program of the Beijing Municipal Education Commission.application/pdfen-AU© 2017 IEEEVisual trackingpart spacesampling201710.1109/TIP.2017.27452042020-11-23