Abstract
To solve the challenge of visual tracking in complex environment, a multi-criteria confidence evaluation strategy is proposed in this paper. Three kinds of criteria are introduced to comprehensively evaluate and analyze the confidence of those tracking results obtained by adaptive spatially-regularized correlation filters (ASRCF). The evaluation result is further utilized to establish the sample management and template updating mechanism, which aims to obtain the best template in the tracking process. The obtained template is used to update both scale filters and position filters in ASRCF. Experimental results on OTB100 and TC128 verify that the proposed method is more robustness compared with other similar algorithms.
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Acknowledgement
This work was partially supported by National natural science fund (61643318) and Key research and development program of Ningxia Hui Autonomous Region (2018YBZD0923).
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Shi, S., Li, N., Ma, Y., Zheng, L. (2021). Multi-criteria Confidence Evaluation for Robust Visual Tracking. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13019. Springer, Cham. https://doi.org/10.1007/978-3-030-88004-0_49
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DOI: https://doi.org/10.1007/978-3-030-88004-0_49
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