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M-PFGMNet: multi-pose feature generation mapping network for visual object tracking

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Abstract

The occlusion and low-resolution environments lead to insufficient feature data and redundant calculations, which affects the accuracy and timeliness of the unmanned detection system in visual object tracking. To solve this issue, this paper proposes the multi-pose feature generation mapping network for visual object tracking (M-PFGMNet). Firstly, M-PFGMNet suggests a distributed generative adversarial network to increase the accuracy of target detection and reconstruct the local-to-global features of the data with insufficient target features. Secondly, M-PFGMNet proposes a multi-pose feature mapping method for real-time target detection, this method can migrate sample data with high similarity between dynamic target and unmanned system. The experimental results show that the algorithm is effective compared with other similar algorithms in the OTB-50/100 public dataset.

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Authors and Affiliations

  1. School of Artificial Intelligence, Henan Institute of Science Technology, Xinxiang, 453003, China

    Lei Cai & Tao Xu

  2. School of Electrical Engineering, Xi’an University of Technology, Xi’an, 710000, China

    Peien Luo

  3. School of Control Science Engineering, Shandong University, Jinan, 250061, China

    Zhenxue Chen

Authors
  1. Lei Cai
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  2. Peien Luo
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  3. Tao Xu
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  4. Zhenxue Chen
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Correspondence to Lei Cai.

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This work was supported by National Key R&D Program of China (2019YFB1311002).

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Cai, L., Luo, P., Xu, T. et al. M-PFGMNet: multi-pose feature generation mapping network for visual object tracking. Multimed Tools Appl 81, 38803–38816 (2022). https://doi.org/10.1007/s11042-022-12875-3

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  • DOI: https://doi.org/10.1007/s11042-022-12875-3

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