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Convolution operators for visual tracking based on spatial–temporal regularization

  • S.I. : ATCI 2019
  • Published:
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Abstract

In recent years, the method based on discriminative correlation filter has been shown excellent performance in short-term visual tracking. However, discriminative correlation filter-based method heavily suffers from the problem of the multiple peaks and model drift in responds maps incurred by occlusion and rotation. To solve the above problem, we proposed convolution operators for visual tracking based on spatial–temporal regularization. Firstly, we add spatial–temporal regularization in loss function, which will guarantee continuity of the model in time. And we use preconditioned conjugate gradient algorithm to obtain filter coefficients. Secondly, we proposed channel reliability to estimate quality of the learned filter and fuse the different reliability coefficients to weight response map in location. We set a threshold to reduce the number of iteration in location and accelerate the compute speed of algorithm. Finally, we use two different correlation filters to estimate location and scale of target, respectively. Extensively experiment in five video sequences show that our tracker has been significantly improved performance in case of occlusion and rotation. The AUC in success plot improves 33.2% than ECO-HC and 41.5% than STRCF, respectively.

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Acknowledgements

This work was supported by Scientific Research Program Funded by Shaanxi Science and Technology Department (2019GY-022, 2019GY-066), National Natural Science Foundation of China (61671362), Science and Technology Program of Weiyang District Science and Technology Department (201923).

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Authors and Affiliations

  1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Peng Wang & Haiyan Wang

  2. Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an, 710072, Shaanxi, China

    Peng Wang

  3. School of Electronics and Information Engineering, Xi’an Technological University, Xi’an, 710021, Shaanxi, China

    Peng Wang, Mengyu Sun, Xiaoyan Li & Yongxia Yang

  4. School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi’an, 710021, Shaanxi, China

    Haiyan Wang

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  1. Peng Wang
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  2. Mengyu Sun
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  3. Haiyan Wang
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  4. Xiaoyan Li
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  5. Yongxia Yang
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Correspondence to Peng Wang.

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Wang, P., Sun, M., Wang, H. et al. Convolution operators for visual tracking based on spatial–temporal regularization. Neural Comput & Applic 32, 5339–5351 (2020). https://doi.org/10.1007/s00521-020-04704-1

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  • DOI: https://doi.org/10.1007/s00521-020-04704-1

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