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TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model
Open accessAbstract
Multi-object tracking is a fundamental vision problem that has been studied for a long time. As deep learning brings excellent performances to object detection algorithms, Tracking by Detection (TBD) has become the mainstream tracking framework. Despite the success of TBD, this two-step method is too complicated to train in an end-to-end manner and induces many challenges as well, such as insufficient exploration of video spatial-temporal information, vulnerability when facing object occlusion, and excessive reliance on detection results. To address these challenges, we propose a concise end-to-end model TubeTK which only needs one step training by introducing the "bounding-tube" to indicate temporal-spatial locations of objects in a short video clip. TubeTK provides a novel direction of multi-object tracking, and we demonstrate its potential to solve the above challenges without bells and whistles. We analyze the performance of TubeTK on several MOT benchmarks and provide empirical evidence to show that TubeTK has the ability to overcome occlusions to some extent without any ancillary technologies like Re-ID. Compared with other methods that adopt private detection results, our one-stage end-to-end model achieves state-of-the-art performances even if it adopts no ready-made detection results. We hope that the proposed TubeTK model can serve as a simple but strong alternative for video-based MOT task. The code and model will be publicly available accompanying this paper.
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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
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 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
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
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
Carreira, J., & Zisserman, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.502 · Cited by 9,570
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Multi-object tracking with Siamese-RPN and adaptive matching strategy
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One-shot multi-object tracking using CNN-based networks with spatial-channel attention mechanism
Li, G., Chen, X., Li, M., Li, W., Li, S., Guo, G., Wang, H., & Deng, H. (2022) · Optics & Laser Technology · doi:10.1016/j.optlastec.2022.108267 · Cited by 15
Self-citationDeepPCT: Single Object Tracking in Dynamic Point Cloud Sequences
Liu, H., Hu, Q., Ma, Y., Xie, K., & Guo, Y. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3232092 · Cited by 16
ORT: Occlusion-robust for multi-object tracking
Han, S., Wang, H., Yu, E., & Hu, Z. (2023) · Fundamental Research · doi:10.1016/j.fmre.2023.02.003 · Cited by 14
Sun, J., Li, Y., Chai, L., & Lu, C. (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/cvpr52729.2023.02108 · Cited by 14
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Meng, W., Duan, S., Ma, S., & Hu, B. (2025) · Journal of Imaging · doi:10.3390/jimaging11050144 · Cited by 15
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Zhou, C., Jiang, M., & Kong, J. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3256761 · Cited by 14
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Sha, M., Shao, D., Guo, J., & Gao, S. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01490 · Cited by 12
Hu, Y., Sun, J., Jin, H., Niu, A., Yan, Q., Zhu, Y., & Zhang, Y. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3558588 · Cited by 12
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Pang, B., Li, Y., Li, J., Li, M., Cao, H., & Lu, C. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i3.16339 · Cited by 13
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Li, Y., Wu, L., Chen, Y., Wang, X., Yin, G., & Wang, Z. (2023) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01273-3 · Cited by 12
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Li, W., Xiong, Y., Yang, S., Deng, S., & Xia, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.16031 · Cited by 13
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Wu, J., Xie, Z., Qin, Y., Jia, L., & Guan, L. (2023) · Measurement and Control · doi:10.1177/00202940231187922 · Cited by 9
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Ma, J., Wu, F., Li, C., Tang, C., Zhang, J., Xu, Z., Li, M., & Liu, D. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3449952 · Cited by 8
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Wang, Y., Zhang, Z., Zhang, N., & Zeng, D. (2021) · Symmetry · doi:10.3390/sym13020266 · Cited by 8
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Zhou, X., Chan, S., Qiu, C., Jiang, X., & Tang, T. (2023) · Sensors · doi:10.3390/s23062956 · Cited by 8
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Self-citationCheng, B., Wang, B., Wang, Q., Chen, W., Shen, T., & Li, Z. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3582679 · Cited by 5
Simple Online Unmanned Aerial Vehicle Tracking with Transformer
Liu, Y., Wang, E., Song, X., Wang, Z., Liu, M., & Shu, W. (2021) · IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom) · doi:10.1109/trustcom53373.2021.00168 · Cited by 4
Multi-Object Tracking with Adaptive Cost Matrix
Wang, M., Lit, B., Jiang, H., & Zhang, J. (2022) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp55362.2022.9948977 · Cited by 4
Metadata
- Authors
- Pang, B., Li, Y., Zhang, Y., Li, M., & Lu, C.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Huazhong University of Science and Technology (CN)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 6307-6317
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035727180
- Cited by
- 303
- References
- 73
- Citations by year
- 2026: 12 · 2025: 22 · 2024: 53 · 2023: 75 · 2022: 78 · 2021: 53 · 2020: 10
- FWCI
- 16.64
- 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)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61772332)
- 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 › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Keywords
- multi-target tracking, tracking-by-detection, end-to-end learning, occlusion handling, video 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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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