Abstract
Correlation Filter-based trackers have achieved excellent performance and run at high frame rates. Recently, Staple, which utilizing a simple combination of a Correlation Filter (using HOG features) and a global color histogram, has achieved excellent performance. It shows strong robustness in challenging situations including motion blur, illumination changes and deformation changes. However, Staple is only a linear combination of two methods. It is not reliable to determine the confidence level only by the peak. In this paper, we propose Gaussian-Staple that utilize a more sensible way of fusion without destroying the response distribution after fusion. Gaussian prior is added to the response of the output, which is used to determine whether to fine tune by local search. Extensive experiments on a commonly used tracking benchmark show that the proposed method significantly improves Staple, and achieves a better performance than other state-of-the-art trackers.
B. Luo—This work was supported in part by National Natural Science Foundation of China under Grant 61472002, 61572030 and 61671018, and Collegiate Natural Science Fund of Anhui Province under Grant KJ2017A014.
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Chen, SB., Ding, CY., Luo, B. (2018). Gaussian-Staple for Robust Visual Object Real-Time Tracking. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_36
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