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Siam R-CNN: Visual Tracking by Re-Detection
Open accessAbstract
We present Siam R-CNN, a Siamese re-detection architecture which unleashes the full power of two-stage object detection approaches for visual object tracking. We combine this with a novel tracklet-based dynamic programming algorithm, which takes advantage of re-detections of both the first-frame template and previous-frame predictions, to model the full history of both the object to be tracked and potential distractor objects. This enables our approach to make better tracking decisions, as well as to re-detect tracked objects after long occlusion. Finally, we propose a novel hard example mining strategy to improve Siam R-CNN's robustness to similar looking objects. Siam R-CNN achieves the current best performance on ten tracking benchmarks, with especially strong results for long-term tracking. We make our code and models available at www.vision.rwth-aachen.de/page/siamrcnn.
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“TransT-GOT method performs 2.2% higher than SiamR-CNN in the main AO metric.”
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“but SiamR-CNN merely runs less than 5 fps in our machine, while our tracker runs at 50 fps”
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“This table shows that the proposed method obtains the best performance, better than other trackers by a significant margin except SiamRCNN [39]”
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Meta-tracker: Fast and Robust Online Adaptation for Visual Object Trackers
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Correlation Tracking via Joint Discrimination and Reliability Learning
Sun, C., Wang, D., Lu, H., & Yang, M. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00058 · Cited by 185
Hyperparameter Optimization for Tracking with Continuous Deep Q-Learning
Dong, X., Shen, J., Wang, W., Liu, Y., Shao, L., & Porikli, F. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00061 · Cited by 171
Long-Term Tracking in the Wild: A Benchmark
Valmadre, J., Bertinetto, L., Henriques, J. F., Ran, T., Vedaldi, A., Smeulders, A. W. M., Torr, P. H. S., & Gavves, E. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_41 · Cited by 157
Self-citationReal-Time ‘Actor-Critic’ Tracking
Chen, B., Wang, D., Li, P., Wang, S., & Lu, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01234-2_20 · Cited by 141
SINT++: Robust Visual Tracking via Adversarial Positive Instance Generation
Wang, X., Li, C., Luo, B., & Tang, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00511 · Cited by 123
Deep Meta Learning for Real-Time Target-Aware Visual Tracking
Choi, J., Kwon, J., & Lee, K. M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00100 · Cited by 118
High-Speed Tracking with Multi-kernel Correlation Filters
Tang, M., Yu, B., Zhang, F., & Wang, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00512 · Cited by 103
ROI Pooled Correlation Filters for Visual Tracking
Sun, Y., Sun, C., Wang, D., He, Y., & Lu, H. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00593 · Cited by 101
Learning Feature Embeddings for Discriminant Model Based Tracking
Zheng, L., Tang, M., Chen, Y., Wang, J., & Lu, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58555-6_45 · Cited by 99
Saffari, A., Godec, M., Pock, T., Leistner, C., & Bischof, H. (2010) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2010.5539937 · Cited by 91
Superpixel-Based Tracking-by-Segmentation Using Markov Chains
Yeo, D., Son, J., Han, B., & Han, J. H. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.62 · Cited by 83
Learning regression and verification networks for long-term visual trackingPreprint
Zhang, Y., Wang, D., Wang, L., Qi, J., & Lu, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.04320 · Cited by 64
Deep Reinforcement Learning with Iterative Shift for Visual Tracking
Ren, L., Yuan, X., Lu, J., Yang, M., & Zhou, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_42 · Cited by 61
Visual Tracking via Spatially Aligned Correlation Filters Network
Zhang, M., Wang, Q., Xing, J., Gao, J. G., Peng, P., Hu, W., & Maybank, S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01219-9_29 · Cited by 58
Bridging the Gap Between Detection and Tracking: A Unified Approach
Huang, L., Zhao, X., & Huang, K. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00410 · Cited by 54
Now you see me: evaluating performance in long-term visual tracking
Lukežič, A., Zajc, L. Č., Vojíř, T., Matas, J., & Kristan, M. (2018) · Cambridge University Engineering Department Publications Database · doi:10.48550/arxiv.1804.07056 · Cited by 50
Joint Representation and Truncated Inference Learning for Correlation Filter Based Tracking
Yao, Y., Wu, X., Lei Zhang, A., Shan, S., & Zuo, W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_34 · Cited by 48
Tracking Holistic Object RepresentationsPreprint
Sauer, A., Aljalbout, E., & Haddadin, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12920 · Cited by 37
Tracking the Known and the Unknown by Leveraging Semantic Information.
Tripathi, A. S., Danelljan, M., Gool, L. V., & Timofte, R. (2019) · Lirias · Cited by 14
Learning Feature Embeddings for Discriminant Model based TrackingPreprint
Zheng, L., Tang, M., Chen, Y., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10414 · Cited by 6
Learning Features with Differentiable Closed-Form Solver for TrackingPreprint
Zheng, L., Tang, M., JinqiaoWang, & Lu, H. (2019) · arXiv (Cornell University) · Cited by 5
Research works printed but not linked: 3
- wu, 2016, Tensorpack
- 10.1007/978-3-030-01261-8_1 · DOI · Paper page
- he, 0, Rethinking imagenet pretraining, ICCV
Cited by
Sorted by citations
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
“This table shows that the proposed method obtains the best performance, better than other trackers by a significant margin except SiamRCNN [39], but SiamR-CNN merely runs less than 5fps in our machine, while our tracker runs at 50 fps.”
MethodDetection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336
Learning 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
“The proposed tracker achieves state-of-the-art performance on multiple challenging short-term and long-term benchmarks, while running at real-time speed, being 6× faster than Siam R-CNN [54].”
BackgroundMethodResultInfluentialMixFormer: End-to-End Tracking with Iterative Mixed Attention
Cui, Y., Jiang, C., Wang, L., & Wu, G. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01324 · Cited by 933
SeqTrack: Sequence to Sequence Learning for Visual Object Tracking
Chen, X., Peng, H., Wang, D., Lu, H., & Han, H. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01400 · Cited by 535
Metadata
- Authors
- Voigtlaender, P., Luiten, J., Torr, P. H. S., & Leibe, B.
- Affiliations
- RWTH Aachen University (DE)
- University of Oxford (GB)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6577-6587
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2990431089
- Cited by
- 115
- References
- 114
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 18 · 2023: 25 · 2022: 26 · 2021: 13 · 2020: 9
- FWCI
- 5.63
- 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)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Multidisciplinary University Research Initiative (EP/N019474/1)
- Engineering and Physical Sciences Research Council (EP/N019474/1)
- H2020 European Research Council (773161)
- 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, two-stage object detection, hard example mining, long-term tracking
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
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