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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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Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
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
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Comaniciu, D., Ramesh, V., & Meer, P. (2003) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2003.1195991 · Cited by 4,560
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
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
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
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
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
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
Learning to detect natural image boundaries using local brightness, color, and texture cues
Martin, D. R., Fowlkes, C. C., & Malik, J. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1273918 · Cited by 2,457
A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation
Perazzi, F., Pont-Tuset, J., McWilliams, B., Gool, L. V., Groß, M. H., & Sorkine‐Hornung, A. (2016) · 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.2016.85 · Cited by 2,039
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
Staple: Complementary Learners for Real-Time Tracking
Bertinetto, L., Valmadre, J., Golodetz, S., Mikšík, O., & Torr, P. H. S. (2016) · 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.2016.156 · Cited by 1,825
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
ATOM: 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
Distractor-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
Fast Online Object Tracking and Segmentation: A Unifying Approach
Wang, Q., Zhang, L., Bertinetto, L., Hu, W., & Torr, P. H. S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00142 · Cited by 1,554
Adaptive Color Attributes for Real-Time Visual Tracking
Danelljan, M., Khan, F. S., Felsberg, M., & van de Weijer, J. (2014) · 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.2014.143 · Cited by 1,541
A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration
Li, Y., & Zhu, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16181-5_18 · Cited by 1,461
Siamese Instance Search for Tracking
Tao, R., Gavves, E., & Smeulders, A. W. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.158 · Cited by 1,208
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
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
Discriminative Correlation Filter with Channel and Spatial Reliability
Lukezic, A., Vojir, T., Zajc, L. C., Matas, J., & Kristan, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.515 · Cited by 1,153
Self-citationOne-Shot Video Object Segmentation
Caelles, S., Maninis, K.-K., Pont-Tuset, J., Leal-Taixe, L., Cremers, D., & Van Gool, L. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.565 · Cited by 927
The Visual Object Tracking VOT2015 Challenge Results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R., Zajc, L. Č., Vojír, T., Häger, G., Lukežič, A., Eldesokey, A., et al. (2015) · 2015 IEEE International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2015.79 · Cited by 895
Self-citationThe 2018 DAVIS Challenge on Video Object SegmentationPreprint
Caelles, S., Montes, A., Maninis, K.-K., Chen, Y., Gool, L. V., Perazzi, F., & Pont-Tuset, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00557 · Cited by 790
Learning to Refine Object Segments
Pinheiro, P. O., Lin, T.-Y., Collobert, R., & Dollár, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46448-0_5 · Cited by 780
A Twofold Siamese Network for Real-Time Object Tracking
He, A., Luo, C., Tian, X., & Zeng, W. (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.2018.00508 · Cited by 713
The Visual Object Tracking VOT2016 Challenge Results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R. P., Čehovin, L., Vojíř, T., Hager, G., Lukežič, A., Fernández, G., et al. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-48881-3_54 · Cited by 689
Self-citationThe Sixth Visual Object Tracking VOT2018 Challenge Results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R. P., Zajc, L. Č., Vojíř, T., Bhat, G., Lukežič, A., Eldesokey, A., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_1 · Cited by 669
Self-citationA Novel Performance Evaluation Methodology for Single-Target Trackers
Kristan, M., Matas, J., Leonardis, A., Vojir, T., Pflugfelder, R. P., Fernandez, G., Nebehay, G., Porikli, F., & Zajc, L. Č. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2516982 · Cited by 613
Self-citationUnveiling 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
FEELVOS: Fast End-To-End Embedding Learning for Video Object Segmentation
Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., & Chen, L.-C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00971 · Cited by 468
Visual Tracking via Adaptive Spatially-Regularized Correlation Filters
Dai, K., Wang, D., Lu, H., Sun, C., & Li, J. (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.2019.00480 · Cited by 448
The Visual Object Tracking VOT2017 Challenge Results
Kristan, M., Leonardis, A., Matas, J., Felsberg, M., Pflugfelder, R. P., Zajc, L. Č., Vojir, T., Hager, G., Lukežič, A., Eldesokey, A., et al. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.230 · Cited by 446
Self-citationOnline Adaptation of Convolutional Neural Networks for Video Object Segmentation
Voigtlaender, P., & Leibe, B. (2017) · Procedings of the British Machine Vision Conference 2017 · doi:10.5244/c.31.116 · Cited by 443
Xu, T., Feng, Z.-H., Wu, X.-J., & Kittler, J. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2919201 · Cited by 411
A Buyer's Guide to Conic Fitting
Fitzgibbon, A., & Fisher, R. B. (1995) · British Machine Vision Conference · doi:10.5244/c.9.51 · Cited by 393
Efficient Video Object Segmentation via Network Modulation
Yang, L., Wang, Y., Xiong, X., Yang, J., & Katsaggelos, A. K. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00680 · Cited by 386
In defense of color-based model-free tracking
Possegger, H., Mauthner, T., & Bischof, H. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298823 · Cited by 370
GradNet: Gradient-Guided Network for Visual Object Tracking
Li, P., Chen, B., Ouyang, W., Wang, D., Yang, X., & Lu, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00626 · Cited by 348
YouTube-VOS: A Large-Scale Video Object Segmentation Benchmark
Xu, N., Yang, L., Fan, Y., Yue, D., Liang, Y., Yang, J., & Huang, T. (2018) · arXiv · doi:10.48550/arxiv.1809.03327 · Cited by 301
Fast and Accurate Online Video Object Segmentation via Tracking Parts
Cheng, J., Tsai, Y., Hung, W.-C., Wang, S., & 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.2018.00774 · Cited by 284
Modeling and Propagating CNNs in a Tree Structure for Visual TrackingPreprint
Nam, H., Baek, M., & Han, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.07242 · Cited by 280
VideoMatch: Matching Based Video Object Segmentation
Hu, Y.-T., Huang, J.-B., & Schwing, A. G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01237-3_4 · Cited by 274
Blazingly Fast Video Object Segmentation with Pixel-Wise Metric Learning
Chen, Y., Pont-Tuset, J., Montes, A., & Gool, L. V. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00130 · Cited by 273
SPM-Tracker: Series-Parallel Matching for Real-Time Visual Object Tracking
Wang, G., Luo, C., Xiong, Z., & Zeng, W. (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.2019.00376 · Cited by 269
Parametric Exponential Linear Unit for Deep Convolutional Neural Networks
Trottier, L., Giguère, P., & Chaib-draa, B. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.00038 · Cited by 234
Deformable Parts Correlation Filters for Robust Visual Tracking
Lukežič, A., Zajc, L. Č., & Kristan, M. (2017) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2716101 · Cited by 112
Self-citationObject Tracking by Reconstruction With View-Specific Discriminative Correlation Filters
Kart, U., Lukežič, A., Kristan, M., Kämäräinen, J., & Matas, J. (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.2019.00143 · Cited by 99
Self-citationDistractor-aware Siamese Networks for Visual Object TrackingPreprint
Zhu, Z. D., Wang, Q., Li, B., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.06048 · Cited by 85
FuCoLoT – A Fully-Correlational Long-Term Tracker
Lukežič, A., Zajc, L. Č., Vojíř, T., Matas, J., & Kristan, M. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-20890-5_38 · Cited by 75
Self-citation
Research works printed but not linked: 3
- lukezic, 2018, FuCoLoT - a fully-correlational longterm tracker, ACCV
- danelljan, 2014, Adaptive color attributes for realtime visual tracking, Comp Vis Patt Recognition, 1090
- 10.1109/CVPR.2017.531 · DOI · Paper page
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
“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.”
BackgroundTransformer Meets Tracker: Exploiting Temporal Context for Robust Visual Tracking
Wang, N., Zhou, W., Wang, J., & Li, H. (2021) · 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/cvpr46437.2021.00162 · Cited by 929
MixFormer: 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
Deep learning in computer vision: A critical review of emerging techniques and application scenarios
Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100134 · Cited by 754
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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