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CLC number: TP391
On-line Access: 2020-10-14
Received: 2019-09-20
Revision Accepted: 2020-04-12
Crosschecked: 2020-09-02
Cited: 0
Clicked: 4357
Citations: Bibtex RefMan EndNote GB/T7714
Asymmetric discriminative correlation filters for visual tracking
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Shui-wang Li, Qian-bo Jiang, Qi-jun Zhao, Li Lu, Zi-liang Feng. Asymmetric discriminative correlation filters for visual tracking[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(10): 1467-1484.
@article{title="Asymmetric discriminative correlation filters for visual tracking",
author="Shui-wang Li, Qian-bo Jiang, Qi-jun Zhao, Li Lu, Zi-liang Feng",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="10",
pages="1467-1484",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900507"
}
%0 Journal Article
%T Asymmetric discriminative correlation filters for visual tracking
%A Shui-wang Li
%A Qian-bo Jiang
%A Qi-jun Zhao
%A Li Lu
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%J Frontiers of Information Technology & Electronic Engineering
%V 21
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%@ 2095-9184
%D 2020
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900507
TY - JOUR
T1 - Asymmetric discriminative correlation filters for visual tracking
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A1 - Qian-bo Jiang
A1 - Qi-jun Zhao
A1 - Li Lu
A1 - Zi-liang Feng
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
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Y1 - 2020
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1900507
Abstract: Discriminative correlation filters (DCF) are efficient in visual tracking and have advanced the field significantly. However, the symmetry of correlation (or convolution) operator results in computational problems and does harm to the generalized translation equivariance. The former problem has been approached in many ways, whereas the latter one has not been well recognized. In this paper, we analyze the problems with the symmetry of circular convolution and propose an asymmetric one, which as a generalization of the former has a weak generalized translation equivariance property. With this operator, we propose a tracker called the asymmetric discriminative correlation filter (ADCF), which is more sensitive to translations of targets. Its asymmetry allows the filter and the samples to have different sizes. This flexibility makes the computational complexity of ADCF more controllable in the sense that the number of filter parameters will not grow with the sample size. Moreover, the normal matrix of ADCF is a block matrix with each block being a two-level block Toeplitz matrix. With this well-structured normal matrix, we design an algorithm for multiplying an N×N two-level block Toeplitz matrix by a vector with time complexity O(NlogN) and space complexity O(N), instead of O(N2). Unlike DCF-based trackers, introducing spatial or temporal regularization does not increase the essential computational complexity of ADCF. Comparative experiments are performed on a synthetic dataset and four benchmarks, including OTB-2013, OTB-2015, VOT-2016, and Temple-Color, and the results show that our method achieves state-of-the-art visual tracking performance.
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