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D3S – A Discriminative Single Shot Segmentation Tracker
Open accessAbstract
Template-based discriminative trackers are currently the dominant tracking paradigm due to their robustness, but are restricted to bounding box tracking and a limited range of transformation models, which reduces their localization accuracy. We propose a discriminative single-shot segmentation tracker - D3S, which narrows the gap between visual object tracking and video object segmentation. A single-shot network applies two target models with complementary geometric properties, one invariant to a broad range of transformations, including non-rigid deformations, the other assuming a rigid object to simultaneously achieve high robustness and online target segmentation. Without per-dataset finetuning and trained only for segmentation as the primary output, D3S outperforms all trackers on VOT2016, VOT2018 and GOT-10k benchmarks and performs close to the state-of-the-art trackers on the TrackingNet. D3S outperforms the leading segmentation tracker SiamMask on video segmentation benchmark and performs on par with top video object segmentation algorithms, while running an order of magnitude faster, close to real-time.
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Yang, Y., & Gu, X. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3350673 · Cited by 4
Template Enhancement and Mask Generation for Siamese Tracking
Ke, X., Li, Y., Yu, Y., & Guo, W. (2020) · IEEE Signal Processing Letters · doi:10.1109/lsp.2020.3048638 · Cited by 4
CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search
Marvasti-Zadeh, S. M., Khaghani, J., Cheng, L., Ghanei-Yakhdan, H., & Kasaei, S. (2021) · British Machine Vision Conference (BMVC) · doi:10.5244/c.35.424 · Cited by 6
Tracking by dynamic template: Dual update mechanism
Liu, J., Wang, Y., Huang, X., & Su, Y. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2022.103456 · Cited by 4
Context‐aware Siamese network for object tracking
Zhang, J., Wang, J., Zhang, H., Miao, M., & Wu, D. (2022) · IET Image Processing · doi:10.1049/ipr2.12629 · Cited by 4
A Dynamic Adjust‐Head Siamese network for object tracking
Qiu, S., Gu, Y., Chen, M., Yuan, Z., Yao, Z., & Zhang, X. (2022) · IET Computer Vision · doi:10.1049/cvi2.12148 · Cited by 4
Metadata
- Authors
- Lukezic, A., Matas, J., & Kristan, M.
- Affiliations
- University of Ljubljana (SI)
- ORCID
- Kristan, Matej
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 7131-7140
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2989778648
- Cited by
- 300
- References
- 58
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 61 · 2023: 72 · 2022: 77 · 2021: 52 · 2020: 13
- FWCI
- 16.50
- Type
- conference paper
- Language
- en
- Open access
- green
- export.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
- Grantová Agentura České Republiky (GA18-05360S, GA18-, 05360S)
- Javna Agencija za Raziskovalno Dejavnost RS (J2-8175, J2-9433, P2-0214)
- 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, video object segmentation, template-based tracking, non-rigid deformation, rigid object tracking, real-time tracking
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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