SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines
Open accessAbstract
Visual tracking problem demands to efficiently perform robust classification and accurate target state estimation over a given target at the same time. Former methods have proposed various ways of target state estimation, yet few of them took the particularity of the visual tracking problem itself into consideration. Based on a careful analysis, we propose a set of practical guidelines of target state estimation for high-performance generic object tracker design. Following these guidelines, we design our Fully Convolutional Siamese tracker++ (SiamFC++) by introducing both classification and target state estimation branch (G1), classification score without ambiguity (G2), tracking without prior knowledge (G3), and estimation quality score (G4). Extensive analysis and ablation studies demonstrate the effectiveness of our proposed guidelines. Without bells and whistles, our SiamFC++ tracker achieves state-of-the-art performance on five challenging benchmarks(OTB2015, VOT2018, LaSOT, GOT-10k, TrackingNet), which proves both the tracking and generalization ability of the tracker. Particularly, on the large-scale TrackingNet dataset, SiamFC++ achieves a previously unseen AUC score of 75.4 while running at over 90 FPS, which is far above the real-time requirement.
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Flagged first, then by citations
Research works printed but not linked: 1
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Cited by
Sorted by citations
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“Some further improvements have been made, such as adding additional branches [42, 45], using deeper architectures [21], exploiting anchor-free architectures [44, 48], and so on.”
BackgroundMethodInfluentialLearning Spatio-Temporal Transformer for Visual Tracking
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“The tracking pipelines of previous trackers [25, 51, 60, 47] are complicated.”
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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
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- Authors
- Xu, Y., Wang, Z., Li, Z., Yuan, Y., & Yu, G.
- Affiliations
- Zhejiang University (CN)
- Megvii (China) (CN)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2020-04-03
- Volume
- 34
- Issue
- 07
- Pages
- 12549-12556
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2998434318
- Cited by
- 974
- References
- 37
- Citations by year
- 2026: 42 · 2025: 125 · 2024: 206 · 2023: 254 · 2022: 205 · 2021: 119 · 2020: 23
- FWCI
- 45.86
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- www.aaai.org/ (unspecified)
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (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, SiamFC, Siamese tracker, target state estimation, real-time tracking, LaSOT
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