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Volume 9 Issue 10
Oct.  2022

IEEE/CAA Journal of Automatica Sinica

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Z. H. Feng, L. P. Yan, Y. Q. Xia, and  B. Xiao,  “An adaptive padding correlation filter with  group feature fusion for robust visual tracking,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1845–1860, Oct. 2022. doi: 10.1109/JAS.2022.105878
Citation: Z. H. Feng, L. P. Yan, Y. Q. Xia, and  B. Xiao,  “An adaptive padding correlation filter with  group feature fusion for robust visual tracking,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 10, pp. 1845–1860, Oct. 2022. doi: 10.1109/JAS.2022.105878

An Adaptive Padding Correlation Filter With Group Feature Fusion for Robust Visual Tracking

doi: 10.1109/JAS.2022.105878
Funds:  This work was supported by the National Key Research and Development Program of China (2018AAA0103203), the National Natural Science Foundation of China (62073036, 62076031), and the Beijing Natural Science Foundation (4202071)
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  • In recent visual tracking research, correlation filter (CF) based trackers become popular because of their high speed and considerable accuracy. Previous methods mainly work on the extension of features and the solution of the boundary effect to learn a better correlation filter. However, the related studies are insufficient. By exploring the potential of trackers in these two aspects, a novel adaptive padding correlation filter (APCF) with feature group fusion is proposed for robust visual tracking in this paper based on the popular context-aware tracking framework. In the tracker, three feature groups are fused by use of the weighted sum of the normalized response maps, to alleviate the risk of drift caused by the extreme change of single feature. Moreover, to improve the adaptive ability of padding for the filter training of different object shapes, the best padding is selected from the preset pool according to tracking precision over the whole video, where tracking precision is predicted according to the prediction model trained by use of the sequence features of the first several frames. The sequence features include three traditional features and eight newly constructed features. Extensive experiments demonstrate that the proposed tracker is superior to most state-of-the-art correlation filter based trackers and has a stable improvement compared to the basic trackers.

     

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    Highlights

    • The discriminative ability of trackers is improved by the adaptive padding
    • Tracking precision can be predicted by the proposed features in first frames
    • Feature groups are dynamically fused to avoid distraction of single feature group

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