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Discriminative correlation tracking based on spatial attention mechanism for low-resolution imaging systems

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Abstract

Low-resolution images are characterized by blurring, less texture information, and lack of detail. Visual object tracking for low-resolution imaging systems remains a challenging task. In this paper, we propose a discriminative correlation tracking algorithm based on a spatial attention mechanism for low-resolution imaging systems (LSDCT) to address these challenges. The key innovations of our proposed algorithm include adjustable windows and a spatial attention mechanism. We design a generic adjustable window to mitigate boundary effects and employ the spatial attention mechanism to highlight the target in low-resolution images. We conduct qualitative and quantitative evaluations on three well-known benchmark datasets: OTB100, TC128, and UAV123. Extensive experimental results indicate that the proposed approach is superior to state-of-the-art trackers.

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Funding

This research was funded by the Natural Science Foundation of China, Grant Number 61806209, and the Key Laboratory of Shaanxi Province Open Foundation, Grant Number SKLIIN-20180103.

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Authors and Affiliations

  1. Xi’an Research Institute of Hi-Tech, Xi’an, 710025, China

    Yueping Huang, Ruitao Lu, Xiaofeng Li, Naixin Qi & Xiaogang Yang

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  1. Yueping Huang
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  2. Ruitao Lu
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  3. Xiaofeng Li
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  4. Naixin Qi
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  5. Xiaogang Yang
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Contributions

The work of methodology, software, validation, and original draft preparation was finished by Y.H.; The work of review and editing was finished by X.L., R.L., and X.Y.; the work of supervision and funding acquisition was finished by X.Y. and R.L.

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Correspondence to Yueping Huang.

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Huang, Y., Lu, R., Li, X. et al. Discriminative correlation tracking based on spatial attention mechanism for low-resolution imaging systems. Vis Comput 38, 1495–1508 (2022). https://doi.org/10.1007/s00371-021-02083-9

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