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Deblurring transformer tracking with conditional cross-attention

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Abstract

In object tracking, motion blur is a common challenge induced by rapid movement of target object or long time exposure of the camera, which leads to poor tracking performance. Traditional solutions usually perform image recovery operations before tracking object. However, most image recovery methods usually have higher computational cost, which decreases the tracking speed. In order to solve the above problems, we propose a deblurring Transformer-based tracking method embedding the conditional cross-attention. The proposed method integrates three important modules: (1) an image quality assessment (IQA) module to estimate image quality; (2) an image deblurring module based on lightweight adversarial network to improve image quality; and (3) a tracking module based on Transformer with conditional cross-attention to enhance the object localization ability. Experimental results on two UAV object tracking benchmarks show that the proposed trackers achieve competitive results compared to several state-of-the-art trackers.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China under Grant 61976042 and 61972068, Innovative Talents Program for Liaoning Universities under Grant LR2019020 and the Liaoning Revitalization Talents Program under Grant XLYC2007023, and was partly supported by Applied Basic Research Project of Liaoning Province under Grant 2022JH2/101300279.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Dalian Minzu University, Liaohexi Road, Dalian, 116600, Liaoning, China

    Fuming Sun, Tingting Zhao & Fasheng Wang

  2. Department of Information Engineering, Harbin Institute of Technology, Xidazhi Street, Harbin, 150006, Heilongjiang, China

    Bing Zhu

  3. School of Electronics and Information Engineering, Liaoning University of Technology, Shiying Street, Jinzhou, 121001, Liaoning, China

    Xu Jia

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  1. Fuming Sun
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  2. Tingting Zhao
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  3. Bing Zhu
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  4. Xu Jia
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  5. Fasheng Wang
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Contributions

F. Sun and B. Zhu conceived this study. T. Zhao and F. Wang conducted the experiment and wrote the initial manuscript. X. Jia and F. Wang reviewed and edited it.

Corresponding author

Correspondence to Fasheng Wang.

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Sun, F., Zhao, T., Zhu, B. et al. Deblurring transformer tracking with conditional cross-attention. Multimedia Systems 29, 1131–1144 (2023). https://doi.org/10.1007/s00530-022-01043-0

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