EACOFT: an energy-aware correlation filter for visual tracking.
Liu, Qiaoyuan; Ren, Jinchang; Wang, Yuru; Wu, Yuanbo; Sun, Haijiang; Zhao, Huimin
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.
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|
|Peer Reviewed||Peer Reviewed|
|Keywords||Visual tracking; Energy-aware correlation filter (EACOFT); Enhanced feature; Top-down and bottom-up strategy|
LIU 2021 EACOFT (AAM)
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