Probabilistic Regression for Visual Tracking
Open accessAbstract
Visual tracking is fundamentally the problem of regressing the state of the target in each video frame. While significant progress has been achieved, trackers are still prone to failures and inaccuracies. It is therefore crucial to represent the uncertainty in the target estimation. Although current prominent paradigms rely on estimating a state-dependent confidence score, this value lacks a clear probabilistic interpretation, complicating its use. In this work, we therefore propose a probabilistic regression formulation and apply it to tracking. Our network predicts the conditional probability density of the target state given an input image. Crucially, our formulation is capable of modeling label noise stemming from inaccurate annotations and ambiguities in the task. The regression network is trained by minimizing the Kullback-Leibler divergence. When applied for tracking, our formulation not only allows a probabilistic representation of the output, but also substantially improves the performance. Our tracker sets a new state-of-the-art on six datasets, achieving 59.8% AUC on LaSOT and 75.8% Success on TrackingNet. The code and models are available at https://github.com/visionml/pytracking.
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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 [47]
Section 2.2 (p. 3) describes recent trackers with a two-stage pipeline: coarse localization followed by bounding-box regression using confidence scores. It says the final box is obtained by gradient-based maximization, but does not say these trackers generate a large number of proposals during tracking or use various post-processing steps to choose a box; the box sampling it mentions occurs during training. (LLM, conf=0.86)
Experimental — accuracy not yet measuredPrevious 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 [10]
The Target Center Regression Module (p. 11) describes a designed optimizer module and says the tracker gradually updates its memory during tracking, supporting the citation’s online-updater example. The paper does not establish that this updater improves nonlinear representation ability, so that stated purpose is not supported. (LLM, conf=0.83)
Experimental — accuracy not yet measuredCurrent prevailing trackers, including offline Siamese trackers and online learning models, are almost all built upon convolutional operations.
“Current prevailing trackers, including both the offline Siamese trackers and the online learning models, are almost all build upon convolutional operations [2, 37, 3, 47].”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [47]
On NFS, PrDiMP achieves an AUC score of 63.5%.
“The previous best method, PrDiMP, achieves an AUC score of 63.5%.”
From Transformer Tracking (2021) · cites this paper as [10]
On NFS, TransT performs better than PrDiMP with a gain of 2.2%.
“Our method performs better than PrDiMP with a gain of 2.2%.”
From Transformer Tracking (2021) · cites this paper as [10]
On OTB2015, TransT achieves comparable results with state-of-the-art algorithms such as PrDiMP and SiamRPN++.
“Table 4 shows that our method achieves comparable results with state-of-the-art algorithms (such as PrDiMP and SiamRPN++).”
From Transformer Tracking (2021) · cites this paper as [10]
STARK-ST101 obtains a success of 67.1% on LaSOT, which is 2.3% higher than Siam R-CNN.
“Furthermore, STARK-ST101 obtains a success of 67.1%, which is 2.3% higher than Siam R-CNN [47], as shown in Fig. 5.”
From Learning Spatio-Temporal Transformer for Visual Tracking (2021) · cites this paper as [47]
Section 5.3 (p. 7) reports LaSOT results for PrDiMP, not STARK-ST101 or Siam R-CNN. A search for those tracker names and the 67.1% success figure found no passage in our copy supporting the stated comparison. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460 · see citing works
Realtime Multi-person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Simon, T., Wei, S.-E., & Sheikh, Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.143 · Cited by 7,539 · see citing works
Adaptive background mixture models for real-time tracking
Stauffer, C., & Grimson, W. E. L. (2003) · 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.1999.784637 · Cited by 6,925 · see citing works
Deep High-Resolution Representation Learning for Human Pose Estimation
Sun, K., Xiao, B., Liu, D., & Wang, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00584 · Cited by 5,984 · see citing works
High-Speed Tracking with Kernelized Correlation Filters
Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2345390 · Cited by 5,753 · 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
Visual object tracking using adaptive correlation filters
Bolme, D., Beveridge, J. R., Draper, B. A., & Lui, Y. M. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539960 · Cited by 3,406 · 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 3× · see citing works
“RPN [52 ] [9 12 34 8 28 4 3] OTB-100 65.8 68.2 67.8 69.1 66.9 69.670.2 66.0 68.4 68.0 69.6 NFS - 48.8 42.2 46.6 58.4 - 53.7 61.0 62.0 63.363.5 Table 5. Comparison with state-of-the-art on the OTB-100 [47] and NFS [14] datasets in terms of overall AUC score. The average value over 5 runs is reported for our approach. A…”
MethodInfluentialUnsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270 · see citing works
High Performance Visual Tracking with Siamese Region Proposal Network
Li, B., Yan, J., Wu, W., Zhu, Z., & Hu, X. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00935 · Cited by 3,023 · see citing works
Wei, S.-E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.511 · Cited by 2,966 · see citing works
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806 · see citing works
ECO: 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
Self-citationPWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Sun, D., Yang, X., Liu, M.-Y., & Kautz, 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.2018.00931 · Cited by 2,727 · see citing works
SiamRPN++: Evolution of Siamese Visual Tracking With Very Deep Networks
Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., & Yan, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00441 · Cited by 2,607 · see citing works
Simple Baselines for Human Pose Estimation and Tracking
Xiao, B., Wu, H., & Wei, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01231-1_29 · Cited by 2,233 · see citing works
Learning Spatially Regularized Correlation Filters for Visual Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.490 · Cited by 2,171 · see citing works
Self-citationA Benchmark and Simulator for UAV Tracking
Mueller, M., Smith, N., & Ghanem, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_27 · Cited by 1,944 · see citing works
Large Margin Methods for Structured and Interdependent Output Variables
Tsochantaridis, I., Joachims, T., Hofmann, T., & Altün, Y. (2005) · MPG.PuRe (Max Planck Society) · Cited by 1,879 · see citing works
Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking
Danelljan, M., Robinson, A., Shahbaz Khan, F., & Felsberg, M. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_29 · Cited by 1,853 · see citing works
Self-citationHierarchical Convolutional Features for Visual Tracking
Ma, C., Huang, J., Yang, X., & Yang, M. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.352 · Cited by 1,811 · see citing works
GOT-10k: A Large High-Diversity Benchmark for Generic Object Tracking in the Wild
Huang, L., Zhao, X., & Huang, K. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2957464 · Cited by 1,809 · 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 5× · see citing works
“The training splits of the LaSOT [13], GOT10k [23], TrackingNet [33] and COCO [30] are used, running 50 epochs with 1000 iterations each.”
MethodInfluentialATOM: Accurate Tracking by Overlap Maximization
Danelljan, M., Bhat, G., Khan, F. S., & Felsberg, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00479 · Cited by 1,591 · see citing works
Self-citationDistractor-Aware Siamese Networks for Visual Object Tracking
Zhu, Z. D., Wang, Q., Li, B., Wu, W., Yan, J., & Hu, W. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01240-3_7 · Cited by 1,575 · see citing works
Learning Discriminative Model Prediction for Tracking
Bhat, G., Danelljan, M., Van Gool, L., & Timofte, R. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00628 · Cited by 1,515 · see citing works
Self-citationDiscriminative Scale Space Tracking
Danelljan, M., Hager, G., Khan, F. S., & Felsberg, M. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2609928 · Cited by 1,315 · see citing works
Self-citationCornerNet: Detecting Objects as Paired Keypoints
Law, H., & Deng, J. (2019) · International Journal of Computer Vision · doi:10.1007/s11263-019-01204-1 · Cited by 1,294 · see citing works
Learning to Track at 100 FPS with Deep Regression Networks
Held, D., Thrun, S., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_45 · Cited by 1,199 · see citing works
TrackingNet: A Large-Scale Dataset and Benchmark for Object Tracking in the Wild
Müller, M., Bibi, A., Giancola, S., Alsubaihi, S., & Ghanem, B. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01246-5_19 · Cited by 1,192 · see citing works
MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization
Zhang, J., Ma, S., & Sclaroff, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10599-4_13 · Cited by 1,150 · see citing works
Hand Keypoint Detection in Single Images Using Multiview Bootstrapping
Simon, T., Joo, H., Matthews, I. A., & Sheikh, Y. A. (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.2017.494 · Cited by 1,147 · see citing works
Tracking Without Bells and Whistles
Bergmann, P., Meinhardt, T., & Leal-Taixe, L. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00103 · Cited by 1,110 · see citing works
Acquisition of Localization Confidence for Accurate Object Detection
Jiang, B., Luo, R., Mao, J., Xiao, T., & Jiang, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01264-9_48 · Cited by 1,059 · see citing works
Struck: Structured Output Tracking with Kernels
Hare, S., Golodetz, S., Saffari, A., Vineet, V., Cheng, M., Hicks, S. L., & Torr, P. H. S. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2509974 · Cited by 994 · see citing works
Learning Dynamic Siamese Network for Visual Object Tracking
Guo, Q., Feng, W., Zhou, C., Huang, R., Wan, L., & Wang, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.196 · Cited by 904 · see citing works
A Tutorial on Energy-Based Learning
LeCun, Y., Chopra, S., Hadsell, R. T., Ranzato, A., & Huang, F. J. (2006) · Cited by 812 · see citing works
The Sixth Visual Object Tracking VOT2018 Challenge Results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. Č., Vojír, T., Bhat, G., Lukežič, A., Eldesokey, A., et al. (2018) · ECCV Workshops · doi:10.1007/978-3-030-11009-3_1 · Cited by 805 · see citing works
Self-citationWang, Q., Teng, Z., Xing, J., Gao, J., Hu, W., & Maybank, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00510 · Cited by 604 · see citing works
VITAL: VIsual Tracking via Adversarial Learning
Song, Y., Ma, C., Wu, X., Gong, L., Bao, L., Zuo, W., Shen, C., Lau, R. W. 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.2018.00937 · Cited by 595 · see citing works
Need for Speed: A Benchmark for Higher Frame Rate Object Tracking
Galoogahi, H. K., Fagg, A., Huang, C., Ramanan, D., & Lucey, S. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.128 · Cited by 542 · see citing works
Unveiling the Power of Deep Tracking
Bhat, G., Johnander, J., Danelljan, M., Khan, F. S., & Felsberg, M. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01216-8_30 · Cited by 514 · see citing works
Self-citationLearning the Model Update for Siamese Trackers
Zhang, L., Gonzalez-Garcia, A., Weijer, J. V. D., Danelljan, M., & Khan, F. S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00411 · Cited by 475 · see citing works
Self-citationXu, T., Feng, Z.-H., Wu, X.-J., & Kittler, J. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2919201 · Cited by 411 · see citing works
Correlation filters with limited boundaries
Galoogahi, H. K., Sim, T., & Lucey, S. (2015) · 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.2015.7299094 · Cited by 362 · see citing works
End-to-End Representation Learning for Correlation Filter Based Tracking
Valmadre, J., Bertinetto, L., Henriques, J., Vedaldi, A., & Torr, P. H. S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.531 · Cited by 274 · see citing works
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 · see citing works
Real-Time Visual Tracking: Promoting the Robustness of Correlation Filter Learning
Sui, Y., Zhang, Z., Wang, G., Tang, Y., & Zhang, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_40 · Cited by 52 · see citing works
LaSOT: A High-quality Benchmark for Large-scale Single Object TrackingPreprint
Fan, H., Lin, L., Yang, F., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1809.07845 · Cited by 22 · see citing works
DCTD: Deep Conditional Target Densities for Accurate Regression
Gustafsson, F., Danelljan, M., Bhat, G., et al. (2019) · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- [16][16] Cl´ ement Godard, Oisin Mac Aodha, and Gabriel J. Brostow. Unsupervised monocular depth estimation with leftright consistency. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR , pages 6602-6611, 2017. 2 -
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 · see citing works
“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.”
BackgroundMethodInfluentialLearning 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
“6% over PrDiMP [12] respectively, using the same ResNet-50 backbone.”
BackgroundMethodInfluentialLearning What to Learn for Video Object Segmentation
Bhat, G., Lawin, F. J., Danelljan, M., Robinson, A., Felsberg, M., Van Gool, L., & Timofte, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58536-5_46 · Cited by 160 · see citing works
Self-citation
- Authors
- Danelljan, M., Van Gool, L., & Timofte, R.
- Affiliations
- ETH Zurich (CH)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 7181-7190
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3013266062
- Cited by
- 798
- References
- 52
- Citations by year
- 2026: 70 · 2025: 121 · 2024: 165 · 2023: 178 · 2022: 151 · 2021: 99 · 2020: 13 · 2019: 1
- FWCI
- 33.65
- 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)
- Lirias (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Nvidia
- Eidgenössische Technische Hochschule Zürich
- 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, probabilistic regression, uncertainty estimation, conditional probability density, label noise, Kullback-Leibler divergence, target state estimation, confidence scores
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