Mean-Shift object tracking with a novel back-projection calculation method

Authors:
LingFeng Wang;HuaiYu Wu;ChunHong Pan
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;Key Laboratory of Machine Perception (MOE), Peking University;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Year:
2009

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Abstract

In this paper, we propose a mean-shift tracking method by using the novel back-projection calculation. The traditional back-projection calculation methods have two main drawbacks: either they are prone to be disturbed by the background when calculating the histogram of target-region, or they only consider the importance of a pixel relative to other pixels when calculating the back-projection of search-region. In order to solve the two drawbacks, we carefully consider the background appearance based on two priors, i.e., texture information of background, and appearance difference between foreground-target and background. Accordingly, our method consists of two basic steps. First, we present a foreground-target histogram approximation method to effectively reduce the disturbance from background. Moreover, the foreground-target histogram is used for back-projection calculation instead of the target-region histogram. Second, a novel back-projection calculation method is proposed by emphasizing the probability that a pixel belongs to the foreground-target. Experiments show that our method is suitable for various tracking scenes and is appealing with respect to robustness.