Abstract
The tracking algorithm based on Siamese networks cannot change the corresponding templates according to appearance changes of targets. Therefore, taking convolution as a similarity measure finds it difficult to collect background information and discriminate background interferents similar to templates, showing poor tracking robustness. In view of this problem, a two-stage tracking algorithm based on the similarity measure for fused features of positive and negative samples is proposed. In accordance with positive and negative sample libraries established online, a discriminator based on measurement for fused features of positive and negative samples is learned to quadratically discriminate a candidate box of hard sample frames. The tracking accuracy and success rate of the algorithm in the OTB2015 benchmark dataset separately reach 92.4% and 70.7%. In the VOT2018 dataset, the algorithm improves the accuracy by nearly 0.2%, robustness by 4.0% and expected average overlap (EAO) by 2.0% compared with the benchmark network SiamRPN++. In terms of the LaSOT dataset, the algorithm is superior to all algorithms compared. Compared with the basic network, its success rate increases by nearly 3.0%, and the accuracy rises by more than 1.0%. Conclusions: The experimental results in the OTB2015, VOT2018 and LaSOT datasets show that the proposed method has a great improvement in the tracking success rate and robustness compared with algorithms based on Siamese networks and particularly, it performs excellently in the LaSOT dataset with a long sequence, occlusion and large appearance changes.
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Acknowledgments
This research was funded by NSFC (No. 62162045, 61866028), Technology Innovation Guidance Program Project (No. 20212BDH81003) and Postgraduate Innovation Special Fund Project (No. YC2021133).
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Huang, K., Chu, J., Qin, P. (2022). Two-stage Object Tracking Based on Similarity Measurement for Fused Features of Positive and Negative Samples. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_49
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