SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
Open accessAbstract
Siamese network based trackers formulate tracking as convolutional feature cross-correlation between target template and searching region. However, Siamese trackers still have accuracy gap compared with state-of-the-art algorithms and they cannot take advantage of feature from deep networks, such as ResNet-50 or deeper. In this work we prove the core reason comes from the lack of strict translation invariance. By comprehensive theoretical analysis and experimental validations, we break this restriction through a simple yet effective spatial aware sampling strategy and successfully train a ResNet-driven Siamese tracker with significant performance gain. Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size. We conduct extensive ablation studies to demonstrate the effectiveness of the proposed tracker, which obtains currently the best results on four large tracking benchmarks, including OTB2015, VOT2018, UAV123, and LaSOT. Our model will be released to facilitate further studies based on this problem.
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- Authors
- Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., & Yan, J.
- Affiliations
- Sensetime (China) (CN)
- Institute of Automation (CN)
- University of Chinese Academy of Sciences (CN)
- Institute of Computing Technology (CN)
- Chinese Academy of Sciences (CN)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 4277-4286
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2963534981
- Cited by
- 2,607
- References
- 49
- Citations by year
- 2026: 143 · 2025: 344 · 2024: 464 · 2023: 497 · 2022: 464 · 2021: 389 · 2020: 247 · 2019: 58 · 2018: 1
- FWCI
- 93.40
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- translation invariance, ResNet50, model size reduction
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