Ocean: Object-Aware Anchor-Free Tracking
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Previous trackers' pipelines are complicated: they generate many box proposals with confidence scores and use post-processing to choose the best bounding box.
“The tracking pipelines of previous trackers [25, 51, 60, 47] are complicated. Specifically, they first generate a large number of box proposals with confidence scores, then use various post-processing to choose the best bounding box as the tracking result.”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [60]
STARK-ST50+AR surpasses previous SOTA trackers like AlphaRef and OceanPlus, getting an EAO of 0.505 on VOT2020.
“The new tracker 'STARK-ST50+AR' surpasses previous SOTA trackers, like AlphaRef and OceanPlus [60], getting an EAO of 0.505.”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [60]
Previous models have to improve nonlinear representation ability by introducing fashion structures, using additional modules, or designing effective online updaters.
“Thus, previous models have to improve the nonlinear representation ability by introducing fashion structures [21, 44, 48], using additional modules [7, 46, 13], designing effective online updaters [2, 47, 10], to name a few.”
From Transformer Tracking (2021) · cites this paper as [48]
Prior work such as SiamRPN, SiamRPN++, and Ocean selects final tracking results using post-processing schemes including cosine window penalty, scale change penalty, and bounding box smoothing.
“In the prior work such as SiamRPN [22], SiamRPN++ [21] and Ocean [48], the final tracking results are selected by post-processing schemes including cosine window penalty, scale change penalty and bounding box smoothing.”
From Transformer Tracking (2021) · cites this paper as [48]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
FCOS: Fully Convolutional One-Stage Object Detection
Tian, Z., Shen, C., Chen, H., & He, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00972 · Cited by 7,013 · see citing works
Fully-Convolutional Siamese Networks for Object Tracking
Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_56 · Cited by 4,546 · see citing works
Wu, Y., Lim, J., & Yang, M.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2388226 · Cited by 3,371 · cited 5× · see citing works
“The standardized OTB-100 [44] benchmark consists of 100 videos.”
BackgroundMethodInfluentialECO: Efficient Convolution Operators for Tracking
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.733 · Cited by 2,805 · see citing works
LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742 · cited 6× · see citing works
“Compared to these benchmark datasets, LaSOT [8] has longer sequences, with an average of 2,500 frames per sequence.”
BackgroundMethodInfluentialA survey of appearance models in visual object tracking
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705 · see citing works
Self-citation
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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
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 · see citing works
“Most of offline Siamese trackers [2, 26, 25, 60, 29] belong to the spatial-only ones, which consider the object tracking as a template-matching between the initial template and the current search region.”
BackgroundMethodInfluentialSelf-citationLightTrack: 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
Self-citationTowards 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
Self-citation
- Authors
- Zhang, Z., Peng, H., Fu, J., Li, B., & Hu, W.
- Affiliations
- Chinese Academy of Sciences (CN)
- Microsoft Research Asia (China) (CN)
- Corresponding author
- Peng, Houwen
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 771-787
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3108519869
- Cited by
- 894
- References
- 10
- Citations by year
- 2026: 41 · 2025: 147 · 2024: 200 · 2023: 241 · 2022: 165 · 2021: 91 · 2020: 9
- FWCI
- 39.02
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (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
- object tracking, Siamese tracker, feature alignment, bounding box regression, tracking robustness, video object 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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