MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking
Open accessAbstract
Standardized benchmarks have been crucial in pushing the performance of computer vision algorithms, especially since the advent of deep learning. Although leaderboards should not be over-claimed, they often provide the most objective measure of performance and are therefore important guides for research. We present MOTChallenge , a benchmark for single-camera Multiple Object Tracking (MOT) launched in late 2014, to collect existing and new data and create a framework for the standardized evaluation of multiple object tracking methods. The benchmark is focused on multiple people tracking, since pedestrians are by far the most studied object in the tracking community, with applications ranging from robot navigation to self-driving cars. This paper collects the first three releases of the benchmark: (i) MOT15 , along with numerous state-of-the-art results that were submitted in the last years, (ii) MOT16 , which contains new challenging videos, and (iii) MOT17 , that extends MOT16 sequences with more precise labels and evaluates tracking performance on three different object detectors. The second and third release not only offers a significant increase in the number of labeled boxes, but also provide labels for multiple object classes beside pedestrians, as well as the level of visibility for every single object of interest. We finally provide a categorization of state-of-the-art trackers and a broad error analysis. This will help newcomers understand the related work and research trends in the MOT community, and hopefully shed some light into potential future research directions.
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On MOT17 private detections, TrackFormer achieves a new state-of-the-art in MOTA (+5.0) and IDF1 (1.7) for methods only trained on CrowdHuman.
“On private detections, we achieve a new state-of-the-art both in terms of MOTA (+5.0) and IDF1 (1.7) for methods only trained on CrowdHuman [45].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [45]
The Introduction (p. not available) identifies MOT17 as a MOTChallenge release, and Section 7’s leaderboard (p. not available) reports MOTA and IDF1, so the source supports the benchmark context. It does not report TrackFormer’s claimed +5.0 MOTA or +1.7 IDF1 gains, or the comparison restricted to methods trained only on CrowdHuman. The performance claim is the citing authors’ own result, not a finding established by this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOn MOT20, TrackFormer is the first method only pretrained on CrowdHuman (CH).
“On MOT20 [13], we introduce the first method only pretrained on CrowdHuman [45] (CH).”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [45]
TrackFormer performs on-par with state-of-the-art results in terms of MOTA without pretraining on CrowdHuman on the MOT17 public detection setting.
“TrackFormer performs on-par with state-of-the-art results in terms of MOTA without pretraining on CrowdHuman [45].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [45]
The MOT17 Detections section describes the benchmark’s public detections, and Section 7’s leaderboard reports MOTA scores for other trackers. Our copy does not address TrackFormer’s performance or whether it was pretrained on CrowdHuman (searched: TrackFormer, CrowdHuman pretraining). (LLM, conf=0.76)
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Opening up Open World Tracking
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Monocular Visual Traffic Surveillance: A Review
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Multi-object tracking review: retrospective and emerging trend
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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 in multiple animal tracking: A survey
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Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
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Visual Object Tracking in First Person Vision
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Dynamic 3D Scene Analysis by Point Cloud Accumulation
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Self-citationPedestrian and vehicle behaviour prediction in autonomous vehicle system — A review
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LettuceTrack: Detection and tracking of lettuce for robotic precision spray in agriculture
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MOTFR: Multiple Object Tracking Based on Feature Recoding
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Focusing on Tracks for Online Multi-Object Tracking
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SORT-YM: An Algorithm of Multi-Object Tracking with YOLOv4-Tiny and Motion Prediction
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Forecasting from LiDAR via Future Object Detection
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Self-citationTubeFormer-DeepLab: Video Mask Transformer
Kim, D., Xie, J., Wang, H., Qiao, S., Yu, Q., Kim, H.-S., Adam, H., Kweon, I. S., & Chen, L.-C. (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/cvpr52688.2022.01354 · Cited by 36 · see citing works
Privacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
Ohno, M., Ukyo, R., Amano, T., Rizk, H., & Yamaguchi, H. (2023) · IEEE International Conference on Pervasive Computing and Communications · doi:10.1109/percom56429.2023.10099061 · Cited by 35 · see citing works
EgoHumans: An Egocentric 3D Multi-Human Benchmark
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One-Shot Multiple Object Tracking in UAV Videos Using Task-Specific Fine-Grained Features
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DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos
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Decode-MOT: How Can We Hurdle Frames to Go Beyond Tracking-by-Detection?
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3D-MuPPET: 3D Multi-Pigeon Pose Estimation and Tracking
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Beyond SOT: Tracking Multiple Generic Objects at Once
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ReidTrack: Reid-only Multi-target Multi-camera Tracking
Specker, A., & Beyerer, J. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00575 · Cited by 27 · see citing works
Transformer-based assignment decision network for multiple object tracking
Psalta, A., Tsironis, V., & Karantzalos, K. G. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.103957 · Cited by 33 · see citing works
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Next Generation Multitarget Trackers: Random Finite Set Methods vs Transformer-based Deep Learning
Pinto, J., Hess, G., Ljungbergh, W., Xia, Y., Svensson, L., & Wymeersch, H. (2021) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion49465.2021.9626990 · Cited by 27 · see citing works
Collaborative Tracking Learning for Frame-Rate-Insensitive Multi-Object Tracking
Liu, Y., Wu, J., & Fu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00914 · Cited by 26 · see citing works
Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey
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Lost and Found: Overcoming Detector Failures in Online Multi-object Tracking
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Enhancing the association in multi‐object tracking via neighbor graph
Liang, T., Lan, L., Zhang, X., Peng, X., & Luo, Z. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22565 · Cited by 23 · see citing works
APPLE MOTS: Detection, Segmentation and Tracking of Homogeneous Objects Using MOTS
de Jong, S., Baja, H., Tamminga, K., & Valente, J. R. P. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3199026 · Cited by 22 · see citing works
NetTrack: Tracking Highly Dynamic Objects with a Net
Zheng, G., Lin, S., Zuo, H., Fu, C., & Pan, J. (2024) · 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/cvpr52733.2024.01811 · Cited by 22 · see citing works
A Decade of Video Analytics at Edge: Training, Deployment, Orchestration, and Platforms
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Is First Person Vision Challenging for Object Tracking?
Dunnhofer, M., Furnari, A., Farinella, G. M., & Micheloni, C. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00304 · Cited by 21 · see citing works
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Detector–Tracker Integration Framework for Autonomous Vehicles Pedestrian Tracking
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Deep Learning for Model-Based Multiobject Tracking
Pinto, J., Hess, G., Ljungbergh, W., Xia, Y., Wymeersch, H., & Svensson, L. (2023) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2023.3289164 · Cited by 31 · see citing works
An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge
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Review: Multiobject tracking in livestock − from farm animal management to state-of-the-art methods
Nidhi, M. H., Liu, K., & Flay, K. J. (2025) · animal · doi:10.1016/j.animal.2025.101503 · Cited by 19 · see citing works
TrackFlow: Multi-Object Tracking with Normalizing Flows
Mancusi, G., Panariello, A., Porrello, A., Fabbri, M., Calderara, S., & Cucchiara, R. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00874 · Cited by 19 · see citing works
High-Temporal-Resolution Object Detection and Tracking Using Images and Events
El-Shair, Z. A., & Rawashdeh, S. A. (2022) · Journal of Imaging · doi:10.3390/jimaging8080210 · Cited by 19 · see citing works
Tracking Passengers and Baggage Items Using Multiple Overhead Cameras at Security Checkpoints
Siddique, A., & Medeiros, H. P. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3225252 · Cited by 20 · see citing works
A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems
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Non-Cooperative Target Pose Estimation based on Improved Iterative Closest Point Algorithm
Zhu, Z., Xiang, W., Huo, J., Yang, M., Zhang, G., & Wei, L. (2022) · Journal of Systems Engineering and Electronics · doi:10.23919/jsee.2022.000001 · Cited by 17 · see citing works
Is an Object-Centric Video Representation Beneficial for Transfer?
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Extracting Unambiguous Drone Signature Using High-Speed Camera
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Du, C., Lin, C., Jin, R., Chai, B., Yao, Y., & Su, S. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17983-2 · Cited by 19 · see citing works
A survey on automated cell tracking: challenges and solutions
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Li, X., & Wu, J. (2022) · Remote Sensing · doi:10.3390/rs14215513 · Cited by 16 · see citing works
I-MuPPET: Interactive Multi-Pigeon Pose Estimation and Tracking
Waldmann, U., Naik, H., Nagy, M., Kano, F., Couzin, I. D., Deußen, O., & Goldlüecke, B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16788-1_31 · Cited by 14 · see citing works
Wong, V. W. H., & Law, K. H. (2023) · Algorithms · doi:10.3390/a16030154 · Cited by 18 · see citing works
Developing a More Reliable Framework for Extracting Traffic Data From a UAV Video
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BioDrone: A Bionic Drone-Based Single Object Tracking Benchmark for Robust Vision
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The use of reinforcement learning algorithms in object tracking: A systematic literature review
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Visual Feature Learning on Video Object and Human Action Detection: A Systematic Review
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Towards Frame Rate Agnostic Multi-object Tracking
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Privacy-preserving pedestrian tracking with path image inpainting and 3D point cloud features
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Visual tracking in camera-switching outdoor sport videos: Benchmark and baselines for skiing
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High-temporal-resolution event-based vehicle detection and tracking
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DetTrack: An Algorithm for Multiple Object Tracking by Improving Occlusion Object Detection
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A Benchmark Database for Animal Re-Identification and Tracking
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Pedestrian multiple-object tracking based on FairMOT and circle loss
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Enhanced robust association for multi-object tracking in multibeam forward-looking sonar video
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Spatio-Temporal Articulation & Coordination Co-attention Graph Network for human motion prediction
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Fast and Robust People Detection in RGB Images
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Small Object Detection and Tracking in Satellite Videos With Motion Informed-CNN and GM-PHD Filter
Aguilar, C. G., Ortner, M., & Zerubia, J. B. (2022) · Frontiers in Signal Processing · doi:10.3389/frsip.2022.827160 · Cited by 13 · see citing works
Hu, N., Wang, S., Wang, X., Cai, Y., Su, D., Nyamsuren, P., Qiao, Y., Yu, J., Hai, B., & Wei, H. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.1047356 · Cited by 13 · see citing works
- Authors
- Dendorfer, P., Os̆ep, A., Milan, A., Schindler, K., Cremers, D., Reid, I., Roth, S., & Leal-Taixé, L.
- Affiliations
- Technical University of Munich (DE)
- ETH Zurich (CH)
- The University of Adelaide (AU)
- Technische Universität Darmstadt (DE)
- Corresponding author
- Dendorfer, Patrick
- ORCID
- Venue
- International Journal of Computer Vision
- Published
- 2020-12-23
- Volume
- 129
- Issue
- 4
- Pages
- 845-881
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W3092779666
- Cited by
- 374
- References
- 162
- Citations by year
- 2026: 87 · 2025: 65 · 2024: 69 · 2023: 61 · 2022: 64 · 2021: 26 · 2020: 2
- FWCI
- 14.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Computer Vision (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- Technischen Universität Darmstadt (submittedVersion, cc-by)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- TUbilio (Technical University of Darmstadt) (submittedVersion)
- Technischen Universität Darmstadt (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Technischen Universität Darmstadt (cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Technische Universität München
- 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
- multiple object tracking, benchmark, single-camera tracking, pedestrian tracking, MOT16, MOT17, error analysis
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