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Visual Tracking by Sampling in Part Space

Huang, Lianghua; Ma, Bo; Shen, Jianbing; He, Hui; Shao, Ling; Porikli, Fatih


In 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...[Show more]

CollectionsANU Research Publications
Date published: 2017
Type: Journal article
Source: IEEE Transactions on Image Processing
DOI: 10.1109/TIP.2017.2745204


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