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EACOFT: an energy-aware correlation filter for visual tracking.

Liu, Qiaoyuan; Ren, Jinchang; Wang, Yuru; Wu, Yuanbo; Sun, Haijiang; Zhao, Huimin

Authors

Qiaoyuan Liu

Jinchang Ren

Yuru Wang

Yuanbo Wu

Haijiang Sun

Huimin Zhao



Abstract

Correlation filter based trackers attribute to its calculation in the frequency domain can efficiently locate targets in a relatively fast speed. This characteristic however also limits its generalization in some specific scenarios. The reasons that they still fail to achieve superior performance to state-of-the-art (SOTA) trackers are possibly due to two main aspects. The first is that while tracking the objects whose energy is lower than the background, the tracker may occur drift or even lose the target. The second is that the biased samples may be inevitably selected for model training, which can easily lead to inaccurate tracking. To tackle these shortcomings, a novel energy-aware correlation filter (EACOFT) based tracking method is proposed, in our approach the energy between the foreground and the background is adaptively balanced, which enables the target of interest always having a higher energy than its background. The samples’ qualities are also evaluated in real time, which ensures that the samples used for template training are always helpful with tracking. In addition, we also propose an optimal bottom-up and top-down combined strategy for template training, which plays an important role in improving both the effectiveness and robustness of tracking. As a result, our approach achieves a great improvement on the basis of the baseline tracker, especially under the background clutter and fast motion challenges. Extensive experiments over multiple tracking benchmarks demonstrate the superior performance of our proposed methodology in comparison to a number of the SOTA trackers.

Citation

LIU, Q., REN, J., WANG, Y., WU, Y., SUN, H. and ZHAO, H. 2021. EACOFT: an energy-aware correlation filter for visual tracking. Pattern recognition [online], 112, article ID 107766. Available from: https://doi.org/10.1016/j.patcog.2020.107766

Journal Article Type Article
Acceptance Date Nov 21, 2020
Online Publication Date Dec 8, 2020
Publication Date Apr 30, 2021
Deposit Date Jan 7, 2021
Publicly Available Date Dec 9, 2021
Journal Pattern recognition
Print ISSN 0031-3203
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 112
Pages 107766
DOI https://doi.org/10.1016/j.patcog.2020.107766
Keywords Visual tracking; Energy-aware correlation filter (EACOFT); Enhanced feature; Top-down and bottom-up strategy
Public URL https://rgu-repository.worktribe.com/output/1084799

Files

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