Real-Time Multiple People Tracking with Deeply Learned Candidate Selection and Person Re-Identification
Open accessAbstract
Online multi-object tracking is a fundamental problem in time-critical video analysis applications. A major challenge in the popular tracking-by-detection framework is how to associate unreliable detection results with existing tracks. In this paper, we propose to handle unreliable detection by collecting candidates from outputs of both detection and tracking. The intuition behind generating redundant candidates is that detection and tracks can complement each other in different scenarios. Detection results of high confidence prevent tracking drifts in the long term, and predictions of tracks can handle noisy detection caused by occlusion. In order to apply optimal selection from a considerable amount of candidates in real-time, we present a novel scoring function based on a fully convolutional neural network, that shares most computations on the entire image. Moreover, we adopt a deeply learned appearance representation, which is trained on large-scale person re-identification datasets, to improve the identification ability of our tracker. Extensive experiments show that our tracker achieves real-time and state-of-the-art performance on a widely used people tracking benchmark.
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TrackFormer's tracking-by-attention approach achieves top performance via global attention without relying on additional motion or appearance models.
“Our tracking-by-attention approach achieves top performance via global attention without relying on additional motion [4, 10] or appearance models [4, 8, 10].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [8]
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STDFormer: Spatial-Temporal Motion Transformer for Multiple Object Tracking
Hu, M., Zhu, X., Wang, H., Cao, S., Liu, C., & Song, Q. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3263884 · Cited by 71 · see citing works
Kalake, L., Wan, W., & Hou, L. (2021) · IEEE Access · doi:10.1109/access.2021.3060821 · Cited by 68 · see citing works
One More Check: Making “Fake Background” Be Tracked Again
Liang, C., Zhang, Z., Xue, Z., Li, B., & Hu, W. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i2.20045 · Cited by 65 · see citing works
Multi-object tracking: a systematic literature review
Hassan, S., Mujtaba, G., Rajput, A., & Fatima, N. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17297-3 · Cited by 67 · see citing works
Aggregate Tracklet Appearance Features for Multi-Object Tracking
Chen, L., Ai, H., Chen, R., & Zhuang, Z. (2019) · IEEE Signal Processing Letters · doi:10.1109/lsp.2019.2940922 · Cited by 63 · see citing works
Self-citationSimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking
Li, J., Ding, Y., Wei, H., Zhang, Y., & Lin, W. (2022) · Sensors · doi:10.3390/s22155863 · Cited by 60 · see citing works
Online multi-object tracking using multi-function integration and tracking simulation training
Yang, J., Ge, H., Yang, J., Tong, Y., & Su, S. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02457-5 · Cited by 60 · see citing works
DEFT: Detection Embeddings for TrackingPreprint
Chaâbane, M. A., Zhang, P., Beveridge, J. R., & O′Hara, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.02267 · Cited by 80 · see citing works
An Adaptive Framework for Multi-Vehicle Ground Speed Estimation in Airborne Videos
Li, J., Chen, S., Zhang, F., Li, E., Yang, T., & Lu, Z. (2019) · Remote Sensing · doi:10.3390/rs11101241 · Cited by 57 · see citing works
Person search: New paradigm of person re-identification: A survey and outlook of recent works
Islam, K. (2020) · Image and Vision Computing · doi:10.1016/j.imavis.2020.103970 · Cited by 57 · see citing works
A survey of detection-based video multi-object tracking
Dai, Y., Hu, Z., Zhang, S., & Liu, L. (2022) · Displays · doi:10.1016/j.displa.2022.102317 · Cited by 59 · see citing works
Simple Unsupervised Multi-Object TrackingPreprint
Karthik, S., Prabhu, A., & Gandhi, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.02609 · Cited by 72 · see citing works
Semi-online Multi-people Tracking by Re-identification
Lan, L., Wang, X., Hua, G., Huang, T. S., & Tao, D. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01314-1 · Cited by 57 · see citing works
Multiple Object Tracking in Deep Learning Approaches: A Survey
Park, Y., Dang, L. M., Lee, S., Han, D., & Moon, H. (2021) · Electronics · doi:10.3390/electronics10192406 · Cited by 57 · see citing works
Tsai, C., Shen, G.-Y., & Nisar, H. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105770 · Cited by 54 · see citing works
MotionTrack: Learning motion predictor for multiple object tracking
Xiao, C., Cao, Q., Zhong, Y., Lan, L., Zhang, X., Luo, Z., & Tao, D. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106539 · Cited by 56 · see citing works
Wong, P. K.-Y., Luo, H., Wang, M., Leung, P. H., & Cheng, J. C. P. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101356 · Cited by 53 · see citing works
UAVSwarm Dataset: An Unmanned Aerial Vehicle Swarm Dataset for Multiple Object Tracking
Wang, C., Su, Y., Wang, J., Wang, T., & Gao, Q. (2022) · Remote Sensing · doi:10.3390/rs14112601 · Cited by 56 · see citing works
Multi-object tracking review: retrospective and emerging trend
Guan, Z., Wang, Z., Zhang, G., Li, L., Zhang, M., Shi, Z., & Jiang, N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11212-y · Cited by 58 · see citing works
Do Different Tracking Tasks Require Different Appearance Models?
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LiDAR-based detection, tracking, and property estimation: A contemporary review
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Deep Learning-Based Robust Multi-Object Tracking via Fusion of mmWave Radar and Camera Sensors
Cheng, L., Sengupta, A., & Cao, S. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3421339 · Cited by 50 · see citing works
Customized Multi-person Tracker
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Dynamic center point learning for multiple object tracking under Severe occlusions
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Spatio-Temporal Point Process for Multiple Object Tracking
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A Review of Deep Learning Techniques for Crowd Behavior Analysis
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EarlyBird: Early-Fusion for Multi-View Tracking in the Bird's Eye View
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Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management
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Multiple Object Forecasting: Predicting Future Object Locations in Diverse Environments
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OneShotDA: Online Multi-Object Tracker With One-Shot-Learning-Based Data Association
Yoon, K., Gwak, J., Song, Y.-M., Yoon, Y.-C., & Jeon, M. (2020) · IEEE Access · doi:10.1109/access.2020.2975912 · Cited by 41 · see citing works
- Authors
- Chen, L., Ai, H., Zhuang, Z., & Shang, C.
- Affiliations
- Tsinghua University (CN)
- Venue
- 2018 IEEE International Conference on Multimedia and Expo (ICME)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-6
- OpenAlex
- W2889935068
- Cited by
- 530
- References
- 21
- Citations by year
- 2026: 25 · 2025: 53 · 2024: 97 · 2023: 75 · 2022: 88 · 2021: 72 · 2020: 72 · 2019: 41 · 2018: 7
- FWCI
- 14.46
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Multimedia and Expo (ICME) (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 › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- multi-target tracking, human tracking, real-time tracking, candidate selection, person re-identification, tracking-by-detection, occlusion handling, fully convolutional network, appearance representation, tracking drift
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.
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