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Robust object tracking with the inverse relocation strategy

  • S.I. : Machine Learning Applications for Self-Organized Wireless Networks
  • Published:
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Abstract

Robust object tracking is a challenging task in multimedia understanding and computer vision. The traditional tracking algorithms only use the forward tracking information while neglecting the inverse information. An inverse relocation strategy is used to learn the translation and scale filters in the proposed tracking algorithm. To begin with, we learn a translation filter using both the forward and the inverse tracking information based on the ridge regression. The object position can be attained using the translation filter by the inverse relocation strategy. Secondly, the scale filter can be attained using the ridge regression and a smooth strategy is adopted to integrate the forward and inverse scale factors. Experiments are performed on the scale variation dataset and the OTB-50 dataset. Extensive experimental results show that the proposed algorithm performs favorably against several state-of-the-art methods in terms of precision and success rate. Meanwhile, the proposed algorithm is also robustness to the deformation to a great extent.

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Acknowledgements

The authors are grateful to the support by National Natural Science Foundation of Shandong Province (Grant: ZR2013FL018).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Information Processing, Universities of Shandong (Shandong Technology and Business University), Yantai, 264005, China

    Shuhe Sun & Zhiyong An

  2. Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, 264005, China

    Zhiyong An

  3. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, 264005, China

    Shuhe Sun, Xinbo Jiang, Bin Zhang & Jianghui Zhang

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  1. Shuhe Sun
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  2. Zhiyong An
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Correspondence to Shuhe Sun.

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Sun, S., An, Z., Jiang, X. et al. Robust object tracking with the inverse relocation strategy. Neural Comput & Applic 31 (Suppl 1), 123–132 (2019). https://doi.org/10.1007/s00521-018-3667-y

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  • DOI: https://doi.org/10.1007/s00521-018-3667-y

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