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Accelerated duality-aware correlation filters for visual tracking

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Abstract

Correlation filters (CF) based tracking methods have attracted considerable attentions for their competitive performance. However, the inherent issues of boundary effect and filter degradation, as well as the scale variation, degrade the tracking accuracy. In addition, the frame-by-frame updating strategy limits the tracking speed, especially in those deep features-based CF trackers. To address these issues, we propose a novel tracker, namely Accelerated Duality-aware Correlation Filters (ADCF), in this paper. In the proposed tracker, dual correlation filters, i.e., translation filter and scale filter, are designed for target localization and scale estimation, respectively. A spatio-temporal regularization term is employed to suppress the boundary effect and filter degradation. Moreover, a model updating strategy named Sparse learning-based Average Peak-to-Correlation Energy (S-APCE) is proposed to accelerate the tracking speed. Finally, an Alternating Direction Method of Multipliers (ADMM) formulation is developed to optimize the ADCF efficiently. Extensive experimental results over six tracking benchmarks prove that the proposed tracker outperforms the state-of-the-art (SOTA) trackers in tracking accuracy and speed.

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  1. https://www.vlfeat.org/matconvnet/.

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Acknowledgements

Many thanks to the anonymous reviewers and editors for the valuable comments and suggestions. This work is partially supported by the National Natural Science Foundation of China (Nos. 61801272 and 61601266), and Natural Science Foundation of Shandong Province (Nos. ZR2020MF127 and ZR2021QD041).

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Correspondence to Mingliang Gao.

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Xu, L., Gao, M., Liu, Z. et al. Accelerated duality-aware correlation filters for visual tracking. Neural Comput & Applic 34, 6241–6256 (2022). https://doi.org/10.1007/s00521-021-06794-x

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