Siamese Box Adaptive Network for Visual Tracking
Open accessAbstract
Most of the existing trackers usually rely on either a multi-scale searching scheme or pre-defined anchor boxes to accurately estimate the scale and aspect ratio of a target. Unfortunately, they typically call for tedious and heuristic configurations. To address this issue, we propose a simple yet effective visual tracking framework (named Siamese Box Adaptive Network, SiamBAN) by exploiting the expressive power of the fully convolutional network (FCN). SiamBAN views the visual tracking problem as a parallel classification and regression problem, and thus directly classifies objects and regresses their bounding boxes in a unified FCN. The no-prior box design avoids hyper-parameters associated with the candidate boxes, making SiamBAN more flexible and general. Extensive experiments on visual tracking benchmarks including VOT2018, VOT2019, OTB100, NFS, UAV123, and LaSOT demonstrate that SiamBAN achieves state-of-the-art performance and runs at 40 FPS, confirming its effectiveness and efficiency. The code will be available at https://github.com/hqucv/siamban.
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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
“In this subsection, we compare our TransT method with twelve state-of-the-art trackers published in 2020 (SiamR-CNN [39], Ocean [48], KYS [3], DCFST [49], SiamFC++ [44], PrDiMP [10], CGACD [13], Sia-mAttn [46], MAML [40], D3S [26], SiamCAR [16], and SiamBAN [5]) and six representative trackers presented before (DiMP […”
BackgroundLearning Spatio-Temporal Transformer for Visual Tracking
Yan, B., Peng, H., Fu, J., Wang, D., & Lu, H. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01028 · Cited by 1,280 · see citing works
“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.”
MethodLightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
Yan, B., Peng, H., Wu, K., Wang, D., Fu, J., & Lu, H. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01493 · Cited by 277 · see citing works
Towards 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
- Chen, Z., Zhong, B., Li, G., Zhang, S., & Ji, R.
- Affiliations
- Huaqiao University (CN)
- University of Chinese Academy of Sciences (CN)
- Harbin Institute of Technology (CN)
- Xiamen University (CN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6667-6676
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035571898
- Cited by
- 1,052
- References
- 51
- Citations by year
- 2026: 69 · 2025: 147 · 2024: 197 · 2023: 248 · 2022: 227 · 2021: 137 · 2020: 27
- FWCI
- 49.22
- 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)
- Funders
- National Natural Science Foundation of China (61772494, 30918014108, 61972167, 61872112)
- National Laboratory of Pattern Recognition (202000012)
- Fundamental Research Funds for the Central Universities (30918014108)
- 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, fully convolutional network, bounding box regression, object classification, real-time tracking
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