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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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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
“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
“STM [45] SiamEM SiamMask [57] SiamMargin [25] Ocean [69] D3S [38] FastOcean AlphaRef [25] OceanPlus [67] STARK -S50+AR STARK -ST50+AR STARK -ST101+AR EAO(↑) 0.”
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TrTr: Visual Tracking with TransformerPreprint
Zhao, M., Okada, K., & Inaba, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.03817 · Cited by 109
Fast Visual Tracking With Siamese Oriented Region Proposal Network
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RPT: Learning Point Set Representation for Siamese Visual Tracking
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A Reliable Sample Selection Strategy for Weakly Supervised Visual Tracking
Liu, S., Xu, X., Zhang, Y., Muhammad, K., & Fu, W. (2022) · IEEE Transactions on Reliability · doi:10.1109/tr.2022.3162346 · Cited by 69
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RPformer: A Robust Parallel Transformer for Visual Tracking in Complex Scenes
Gu, F., Lu, J., & Cai, C. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3170972 · Cited by 65
Joint Classification and Regression for Visual Tracking with Fully Convolutional Siamese Networks
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RGBD1K: A Large-Scale Dataset and Benchmark for RGB-D Object Tracking
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Yang, D., He, J., Ma, Y., Yu, Q., & Zhang, T. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00928 · Cited by 60
Deep Learning-Based Object Tracking in Satellite Videos: A comprehensive survey with a new dataset
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CorrFormer: Context-aware tracking with cross-correlation and transformer
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High-Performance Transformer Tracking
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Handling Occlusion in UAV Visual Tracking With Query-Guided Redetection
Xue, Y., Shen, T., Jin, G., Tan, L., Wang, N., Wang, L., & Gao, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3440378 · Cited by 56
Do Different Tracking Tasks Require Different Appearance Models?
Wang, Z., Zhao, H., Li, Y., Wang, S., Torr, P. H. S., & Bertinetto, L. (2021) · Oxford University Research Archive (ORA) (University of Oxford) · doi:10.48550/arxiv.2107.02156 · Cited by 56
Learning Localization-Aware Target Confidence for Siamese Visual Tracking
Nie, J., He, Z., Yang, Y., Gao, M., & Dong, Z. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3206668 · Cited by 53
Multi-modal visual tracking: Review and experimental comparison
Zhang, P., Wang, D., & Lu, H. (2024) · Computational Visual Media · doi:10.1007/s41095-023-0345-5 · Cited by 53
Learning residue-aware correlation filters and refining scale for real-time UAV tracking
Li, S., Liu, Y., Zhao, Q., & Feng, Z. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108614 · Cited by 47
Tiny Object Tracking: A Large-Scale Dataset and a Baseline
Zhu, Y., Li, C., Liu, Y., Wang, X., Tang, J., Luo, B., & Huang, Z. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3239529 · Cited by 47
Tracking by Joint Local and Global Search: A Target-Aware Attention-Based Approach
Wang, X., Tang, J., Luo, B., Wang, Y., Tian, Y., & Wu, F. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3083933 · Cited by 46
EMAT: Efficient feature fusion network for visual tracking via optimized multi-head attention
Wang, J., Lai, C., Wang, Y., & Zhang, W. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106110 · Cited by 47
Visual Object Tracking in First Person Vision
Dunnhofer, M., Furnari, A., Farinella, G. M., & Micheloni, C. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01694-6 · Cited by 52
EANTrack: An Efficient Attention Network for Visual Tracking
Gu, F., Lu, J., Cai, C., Zhu, Q., & Ju, Z. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3319676 · Cited by 44
Automatic Object Tracking and Segmentation Using Unsupervised SiamMask
Noor, S., Waqas, M., Saleem, M. I., & Minhas, H. N. (2021) · IEEE Access · doi:10.1109/access.2021.3101054 · Cited by 46
CMAT: Integrating Convolution Mixer and Self-Attention for Visual Tracking
Wang, J., Yin, P., Wang, Y., & Yang, W. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3264851 · Cited by 42
Tang, Y., Liu, Y., & Huang, H. B. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103535 · Cited by 40
Mandel, T., Jimenez, M., Risley, E., Nammoto, T., Williams, R. T., Panoff, M., Ballesteros, M., & Suarez, B. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109107 · Cited by 42
Tan, H., Zhang, X., Zhang, Z., Lan, L., Zhang, W., & Luo, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3049970 · Cited by 38
Online Learning Samples and Adaptive Recovery for Robust RGB-T Tracking
Liu, J., Luo, Z., & Xiong, X. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3288853 · Cited by 34
Switch and Refine: A Long-Term Tracking and Segmentation Framework
Xu, X., Zhao, J., Wu, J., & Shen, F. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3210245 · Cited by 33
A Siamese network-based tracking framework for hyperspectral video
Tang, Y., Huang, H. B., Liu, Y., & Li, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07712-5 · Cited by 34
Han, W., Dong, X., Zhang, Y., Crandall, D., Xu, C., & Shen, J. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3400873 · Cited by 33
Toward Accurate Pixelwise Object Tracking via Attention Retrieval
Zhang, Z., Liu, Y., Li, B., Hu, W., & Peng, H. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3117077 · Cited by 31
TRAT: Tracking by attention using spatio-temporal features
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Siamese-Based Twin Attention Network for Visual Tracking
Bao, H., Shu, P., Zhang, H., & Liu, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3207202 · Cited by 33
Zhao, K., Liu, H., Su, N., Xu, C., Yan, Y., & Feng, S. (2023) · Remote Sensing · doi:10.3390/rs15041107 · Cited by 33
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
Learning Cross-Attention Discriminators via Alternating Time–Space Transformers for Visual Tracking
Wang, W., Zhang, K., Su, Y., Wang, J., & Wang, Q. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3282905 · Cited by 31
NCSiam: Reliable Matching via Neighborhood Consensus for Siamese-Based Object Tracking
Lai, P., Cheng, G., Zhang, M., Ning, J., Zheng, X., & Han, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3329669 · Cited by 31
Learning modality feature fusion via transformer for RGBT-tracking
Cai, Y., Sui, X., Gu, G., & Chen, Q. (2023) · Infrared Physics & Technology · doi:10.1016/j.infrared.2023.104819 · Cited by 29
Learning object-uncertainty policy for visual tracking
He, X., & Chen, C. Y. (2021) · Information Sciences · doi:10.1016/j.ins.2021.09.002 · Cited by 29
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-Based Beam Search
Wang, X., Chen, Z., Jiang, B., Tang, J., Luo, B., & Tao, D. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3208437 · Cited by 29
Joint Correlation and Attention Based Feature Fusion Network for Accurate Visual Tracking
Yang, Y., & Gu, X. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3251027 · Cited by 30
Correlation-Embedded Transformer Tracking: A Single-Branch Framework
Xie, F., Yang, W., Wang, C., Chu, L., Cao, Y., Ma, C., & Zeng, W. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3448254 · Cited by 35
Li, S., Liu, Y., Zhao, Q., & Feng, Z. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00131 · Cited by 25
Rank-Based Filter Pruning for Real-Time UAV Tracking
Wang, X., Zeng, D., Zhao, Q., & Li, S. (2022) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme52920.2022.9859656 · Cited by 28
OmniTracker: Unifying Visual Object Tracking by Tracking-With-Detection
Wang, J., Wu, Z., Chen, D., Luo, C., Dai, X., Yuan, L., & Jiang, Y. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3529926 · Cited by 47
Learning to Rank Proposals for Siamese Visual Tracking
Tang, F., & Ling, Q. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3120305 · Cited by 26
SiamCAF: Complementary Attention Fusion-Based Siamese Network for RGBT Tracking
Xue, Y., Zhang, J., Lin, Z., Li, C., Huo, B., & Zhang, Y. (2023) · Remote Sensing · doi:10.3390/rs15133252 · Cited by 25
Hu, Q., Wei, X., Guo, H., Xu, H., Li, C., He, W., & Pei, B. (2023) · Applied Acoustics · doi:10.1016/j.apacoust.2023.109363 · Cited by 24
Wu, P., Li, Y., & Dong, X. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11348-x · Cited by 29
Target-Aware Transformer Tracking
Zheng, Y., Zhang, Y., & Xiao, B. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3276061 · Cited by 24
Integrating Boxes and Masks: A Multi-Object Framework for Unified Visual Tracking and Segmentation
Xu, Y., Yang, Z., & Yang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00893 · Cited by 23
Adaptive temporal feature modeling for visual tracking via cross-channel learning
Wang, Y., Zhang, W., Lai, C., & Wang, J. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110380 · Cited by 23
Is First Person Vision Challenging for Object Tracking?
Dunnhofer, M., Furnari, A., Farinella, G. M., & Micheloni, C. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00304 · Cited by 24
Towards Universal Modal Tracking With Online Dense Temporal Token Learning
Zheng, Y., Zhong, B., Liang, Q., Zhang, S., Li, G., Li, X., & Ji, R. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3593543 · Cited by 21
Zhang, Z., Zhu, X., Zhao, D., Arun, P. V., Zhou, H., Qian, K., & Hu, J. (2022) · Remote Sensing · doi:10.3390/rs14235958 · Cited by 21
Hierarchical Feature Embedding for Visual Tracking
Pi, Z., Wan, W., Sun, C., Gao, C., Sang, N., & Li, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_25 · Cited by 23
Advances in Deep Learning Methods for Visual Tracking: Literature Review and Fundamentals
Zhang, X., Jiang, R.-H., Fan, C., Tong, T.-Y., Wang, T., & Huang, P. (2021) · Machine Intelligence Research · doi:10.1007/s11633-020-1274-8 · Cited by 22
Fisher Pruning for Real-Time UAV Tracking
Wu, W., Zhong, P., & Li, S. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892373 · Cited by 20
Cai, R., Li, J., Tan, Y., Tang, J., & Chen, X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113374 · Cited by 19
A Discriminative Single-Shot Segmentation Network for Visual Object Tracking
Lukežič, A., Matas, J., & Kristan, M. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3137933 · Cited by 20
Self-citationVisual object tracking via non-local correlation attention learning
Gao, L., Liu, P., Ning, J., & Li, Y. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109666 · Cited by 20
Spatio-Temporal Contextual Learning for Single Object Tracking on Point Clouds
Gao, J., Yan, X., Zhao, W., Lyu, Z., Liao, Y., & Zheng, C. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3233562 · Cited by 20
SiamSTC: Updatable Siamese tracking network via Spatio-Temporal Context
Wei, B., Chen, H., Ding, Q., & Luo, H. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110286 · Cited by 20
The First Visual Object Tracking Segmentation VOTS2023 Challenge Results
Kristan, M., Matas, J., Danelljan, M., Felsberg, M., Chang, H. J., Zajc, L. Č., Lukežič, A., Drbohlav, O., Zhang, Z., Tran, K.-T., et al. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00195 · Cited by 24
Self-citation
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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