SiamCAR: Siamese Fully Convolutional Classification and Regression for Visual Tracking
Open accessAbstract
By decomposing the visual tracking task into two subproblems as classification for pixel category and regression for object bounding box at this pixel, we propose a novel fully convolutional Siamese network to solve visual tracking end-to-end in a per-pixel manner. The proposed framework SiamCAR consists of two simple subnetworks: one Siamese subnetwork for feature extraction and one classification-regression subnetwork for bounding box prediction. Different from state-of-the-art trackers like Siamese-RPN, SiamRPN++ and SPM, which are based on region proposal, the proposed framework is both proposal and anchor free. Consequently, we are able to avoid the tricky hyper-parameter tuning of anchors and reduce human intervention. The proposed framework is simple, neat and effective. Extensive experiments and comparisons with state-of-the-art trackers are conducted on challenging benchmarks including GOT-10K, LaSOT, UAV123 and OTB-50. Without bells and whistles, our SiamCAR achieves the leading performance with a considerable real-time speed. The code is available at https://github.com/ohhhyeahhh/SiamCAR.
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Previous Siamese trackers only exploit spatial information for tracking, while online methods use historical predictions for model updates.
“Previous Siamese trackers [25, 51, 14, 6] only exploit the spatial information for tracking, while online methods [54, 57, 9, 3] use historical predictions for model updates.”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [14]
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Chen, X., Yan, B., Zhu, J., Wang, D., Yang, X., & Lu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00803 · Cited by 1,588 · see citing works
Learning Spatio-Temporal Transformer for Visual Tracking
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“Previous Siamese trackers [25, 51, 14, 6] only exploit the spatial information for tracking, while online methods [54, 57, 9, 3] use historical predictions for model updates.”
MethodTowards Accurate Pixel-wise Object Tracking by Attention Retrieval
Zhang, Z., Li, B., Hu, W., & Peng, H. (2020) · arXiv · doi:10.48550/arxiv.2008.02745 · Cited by 32 · see citing works
- Authors
- Guo, D., Wang, J., Cui, Y., Wang, Z., & Chen, S.
- Affiliations
- Zhejiang University of Technology (CN)
- Tianjin University of Technology (CN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6268-6276
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035511673
- Cited by
- 984
- References
- 50
- Citations by year
- 2026: 61 · 2025: 135 · 2024: 218 · 2023: 249 · 2022: 202 · 2021: 107 · 2020: 12
- FWCI
- 43.91
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- 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
- visual object tracking, Siamese network, fully convolutional network, pixel classification, bounding box regression, real-time tracking
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