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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 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]
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“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]
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“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]
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Metadata
- 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
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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