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Multi-view region proposal network predictive learning for tracking

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Abstract

Visual tracking is one of the most challenging problems in computer vision. Most state-of-the-art visual trackers suffer from three challenging problems: nondiverse discriminate feature representation, coarse object locator, and limited quantities of positive samples. In this paper, a multi-view multi-expert region proposal prediction algorithm for tracking is proposed to solve the above problems concurrently in one framework. The proposed algorithm integrates multiple views and exploits powerful multiple sources of information, which can solve nondiverse discriminate feature representation problem effectively. It builds multiple SVM classifier models on the expanded bounding boxes and adds the regional suggestion network module to accurately optimize it to predict optimal object location, which naturally alleviates the coarse object locator and limited quantities of positive samples problems at the same time. A comprehensive evaluation of the proposed approach on various benchmark sequences has been performed. The evaluation results demonstrate our method can significantly improve the tracking performance by combining the advantages of lightweight region proposal network predictive learning model and multi-view expert groups. The experimental results demonstrate the proposed approach outperforms other state-of-the-art visual trackers.

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Acknowledgements

The author would like to thank the anonymous reviewers for their helpful comments on an earlier draft of this paper. The work was supported in part by the National Natural Science Foundation of China under Grant 62072286 and Grant 61572296.

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Shandong Technology And Business University, Yantai, 264005, China

    Wen Guo, Dong Li, Bowen Liang & Bin Shan

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  1. Wen Guo
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  2. Dong Li
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Correspondence to Wen Guo.

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Communicated by B. Bao.

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Guo, W., Li, D., Liang, B. et al. Multi-view region proposal network predictive learning for tracking. Multimedia Systems 29, 333–346 (2023). https://doi.org/10.1007/s00530-022-01001-w

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