Autocorrelated correlation filter for visual tracking

Weichun Liu; Dongdong Li; Xiaoan Tang

doi:10.1117/1.JEI.28.3.033038

28 June 2019 Autocorrelated correlation filter for visual tracking

Weichun Liu, Dongdong Li, Xiaoan Tang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033038 (June 2019). https://doi.org/10.1117/1.JEI.28.3.033038

Abstract

Recently, discriminative correlation filters (DCF) have achieved enormous popularity in the tracking community due to high efficiency and fair robustness. With a circular structure, DCFs transform computationally consuming spatial correlation into efficient element-wise operation in the Fourier domain. We argue that this element-wise solution can be derived only in the case of single-channel features. In terms of tracking with multichannel features, this element-wise solution trains each feature dimension independently and fails to learn a joint correlation filter. To tackle this problem, we propose a rigorous solution to closed-form correlation filter tracking. This rigorous solution can be computed pixel by pixel from a small linear equation system. Experimental results demonstrate that our rigorous pixel-wise solution achieves better tracking performance than the baseline element-wise solution.

Citation Download Citation

Weichun Liu, Dongdong Li, and Xiaoan Tang "Autocorrelated correlation filter for visual tracking," Journal of Electronic Imaging 28(3), 033038 (28 June 2019). https://doi.org/10.1117/1.JEI.28.3.033038

Received: 8 March 2019; Accepted: 7 June 2019; Published: 28 June 2019

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