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A robust tracking algorithm with on online detector and high-confidence updating strategy

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Abstract

The discriminative correlation filter-based tracking algorithms cannot correctly track the target if the target is occluded or out of view and reappears in the field of vision, and they cannot ensure the tracking model is updated correctly if the tracking information is not correct. In this paper, a robust correlation tracking algorithm is proposed. Here, a failure detection strategy, which is based on the maximal confidence score and peak-to-sidelobe ratio to detect or measure the reliability of the tracking result, is integrated into the tracker. Moreover, the redetection module based on the keypoints matching method for consensus voting is introduced into the proposed tracking algorithm to redetect objects in case of tracking failure. In addition, an adaptive high-confidence updating method is proposed to avoid error model information introduced into the tracker caused by occlusions, out-of-view or illumination changes, where the learning rate is determined by the change rate of the confidence map. The OTB-2015 dataset and VOT-2016 dataset are used to evaluate the performance of the proposed tracking algorithm. The experimental results show that the proposed tracking algorithm performs better than most of the state-of-the-art trackers, and it has higher accuracy and robustness than the DSST tracker.

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Acknowledgements

This work was partially supported by the Natural Science Foundation of China (No. 61603274), the Natural Science Foundation of Tianjin (No. 18JCYBJC87700), South African National Research Foundation Grants (Nos. 112108 and 112142), and South African National Research Foundation Incentive Grant (No. 114911), and Eskom Tertiary Education Support Programme Grant.

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Author notes

  1. Enzeng Dong and Mengtao Deng contributed equally to this work.

Authors and Affiliations

  1. Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, Tianjin University of Technology, Tianjin, 300384, China

    Enzeng Dong & Mengtao Deng

  2. Department of Electrical and Mining Engineering, University of South Africa, Florida, 1710, South Africa

    Zenghui Wang

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  1. Enzeng Dong
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  2. Mengtao Deng
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  3. Zenghui Wang
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Correspondence to Enzeng Dong.

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Appendices

Appendix

This appendix contains additional experimental results.

Analyze the effectiveness of PSR

Here, we added some experiment to analyze the effectiveness of PSR in Sect. 3.2. As shown in Fig. 14, the experimental results show that the maximal confidence score and the PSR can reflect the confidence degree about the tracking performance to some extent. Therefore, the maximal confidence score and PSR are used as reference for judging whether the tracking result is reliable.

Fig. 14
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The changes in the confidence map of the 5 image sequences

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Table 5 Average overlap accuracy (OP) for each sequence on the out-of-view dataset (%)
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Fig. 15
figure 15

The average precision over the eleven challenges on the OTB-2015 dataset

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Additional results on out-of-view dataset

Here, we provide further experimental evaluation on 14 out-of-view videos in the OTB-2015 dataset. As shown in Table 5, it shows the average overlap accuracy of the proposed method for each sequence and is compared to seven state-of-the-art trackers.

Additional results on OTB-2015 dataset

Here, as shown in Fig. 15, we give the average precision for each of the 11 challenging attributes.

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Dong, E., Deng, M. & Wang, Z. A robust tracking algorithm with on online detector and high-confidence updating strategy. Vis Comput 37, 567–585 (2021). https://doi.org/10.1007/s00371-020-01824-6

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