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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Liu, Z., Wang, X., Wang, C., Liu, W., & Bai, X. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3524670 · Cited by 126 · see citing works
Modelling Ambiguous Assignments for Multi-Person Tracking in Crowds
Stadler, D., & Beyerer, J. (2022) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw54805.2022.00019 · Cited by 106 · see citing works
SMILEtrack: SiMIlarity LEarning for Occlusion-Aware Multiple Object Tracking
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Focus On Details: Online Multi-Object Tracking with Diverse Fine-Grained Representation
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UTM: A Unified Multiple Object Tracking Model with Identity-Aware Feature Enhancement
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Self-citationLooking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
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A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
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A General Recurrent Tracking Framework without Real Data
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Self-citationSTDFormer: Spatial-Temporal Motion Transformer for Multiple Object Tracking
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A Multitask Benchmark Dataset for Satellite Video: Object Detection, Tracking, and Segmentation
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One More Check: Making “Fake Background” Be Tracked Again
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An Occlusion-aware Multi-target Multi-camera Tracking System
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Do Different Tracking Tasks Require Different Appearance Models?
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Self-citationGMOT-40: A Benchmark for Generic Multiple Object Tracking
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Recent Advances in Intelligent Processing of Satellite Video: Challenges, Methods, and Applications
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Toward Real-Time UAV Multi-Target Tracking Using Joint Detection and Tracking
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MOTFR: Multiple Object Tracking Based on Feature Recoding
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BPMTrack: Multi-Object Tracking With Detection Box Application Pattern Mining
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Multi-Target Multi-Camera Tracking With Optical-Based Pose Association
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Human Trajectory Prediction with Momentary Observation
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Self-citationEfficient Video Instance Segmentation via Tracklet Query and Proposal
Wu, J., Yarram, S., Liang, H., Lan, T., Yuan, J., Eledath, J., & Medioni, G. 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/cvpr52688.2022.00103 · Cited by 39 · see citing works
Online multiple object tracking using joint detection and embedding network
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Yolo-3DMM for Simultaneous Multiple Object Detection and Tracking in Traffic Scenarios
Liu, L., Song, X., Song, H., Sun, S., Han, X.-F., Akhtar, N., & Mian, A. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3360875 · Cited by 38 · see citing works
Semi-TCL: Semi-Supervised Track Contrastive Representation LearningPreprint
Li, W., Xiong, Y., Yang, S., Xu, M., Wang, Y., & Xia, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.02396 · Cited by 35 · see citing works
Tracklets Predicting Based Adaptive Graph TrackingPreprint
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Decode-MOT: How Can We Hurdle Frames to Go Beyond Tracking-by-Detection?
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Joint Detection and Multi-Object Tracking with Graph Neural NetworksPreprint
Wang, Y., Weng, X., & Kitani, K. (2020) · arXiv (Cornell University) · Cited by 34 · see citing works
Multi-Pedestrian Tracking with Clusters
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Multiple Pedestrian Tracking Under Occlusion: A Survey and Outlook
Sun, Z., Wei, G., Fu, W. E., Ye, M., Jiang, K., Liang, C., Zhu, T., He, T., & Mukherjee, M. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3481425 · Cited by 35 · see citing works
ReidTrack: Reid-only Multi-target Multi-camera Tracking
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Improving Multi-Target Multi-Camera Tracking by Track Refinement and Completion
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Transformer-based assignment decision network for multiple object tracking
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MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking
Zhu, Z., Nie, J., Wu, H., He, Z., & Gao, M. (2022) · Sensors · doi:10.3390/s22228650 · Cited by 29 · see citing works
Multi-Object Tracking with Multiple Cues and Switcher-Aware Classification
Feng, W., Li, B., & Ouyang, W. (2022) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta56598.2022.10034575 · Cited by 28 · see citing works
LMOT: Efficient Light-Weight Detection and Tracking in Crowds
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Lost and Found: Overcoming Detector Failures in Online Multi-object Tracking
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NCT:noise-control multi-object tracking
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PAS Tracker: Position-, Appearance- and Size-Aware Multi-object Tracking in Drone Videos
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Fusion detection and ReID embedding with hybrid attention for multi-object tracking
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Accurate Object Association and Pose Updating for Semantic SLAM
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Multiple Object Tracking by Trajectory Map Regression with Temporal Priors Embedding
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AOH: Online Multiple Object Tracking With Adaptive Occlusion Handling
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Tracking Game: Self-adaptative Agent based Multi-object Tracking
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An Interactively Motion-Assisted Network for Multiple Object Tracking in Complex Traffic Scenes
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SegDQ: Segmentation assisted multi-object tracking with dynamic query-based transformers
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An Object Point Set Inductive Tracker for Multi-Object Tracking and Segmentation
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Pixel-Guided Association for Multi-Object Tracking
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Multiple Object Tracking and Forecasting: Jointly Predicting Current and Future Object Locations
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Compensation Tracker: Reprocessing Lost Object for Multi-Object Tracking
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Large Scale Real-World Multi-person Tracking
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A Simple but Effective Method for Balancing Detection and Re-Identification in Multi-Object Tracking
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Fast Vehicle Turning-Movement Counting using Localization-based Tracking
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- 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
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