MOTS: Multi-Object Tracking and Segmentation
Open accessAbstract
This paper extends the popular task of multi-object tracking to multi-object tracking and segmentation (MOTS). Towards this goal, we create dense pixel-level annotations for two existing tracking datasets using a semi-automatic annotation procedure. Our new annotations comprise 65,213 pixel masks for 977 distinct objects (cars and pedestrians) in 10,870 video frames. For evaluation, we extend existing multi-object tracking metrics to this new task. Moreover, we propose a new baseline method which jointly addresses detection, tracking, and segmentation with a single convolutional network. We demonstrate the value of our datasets by achieving improvements in performance when training on MOTS annotations. We believe that our datasets, metrics and baseline will become a valuable resource towards developing multi-object tracking approaches that go beyond 2D bounding boxes. We make our annotations, code, and models available at https://www.vision.rwth-aachen.de/page/mots.
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TrackFormer achieves state-of-the-art results on the MOTS20 challenge.
“Furthermore, we demonstrate the extension with a mask prediction head and show state-of-the-art results on the Multi-Object Tracking and Segmentation (MOTS20) challenge [52].”
From TrackFormer: Multi-Object Tracking with Transformers (2022) · cites this paper as [52]
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STEP: Segmenting and Tracking Every PixelPreprint
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Opening up Open World Tracking
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Object detection and tracking on UAV RGB videos for early extraction of grape phenotypic traits
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- Authors
- Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B. B. G., Geiger, A., & Leibe, B.
- Affiliations
- RWTH Aachen University (DE)
- RWTH Aachen Univ.
- Max Planck Institute for Intelligent Systems (DE)
- Max Planck Society (DE)
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 7934-7943
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2913562503
- Cited by
- 658
- References
- 71
- Citations by year
- 2026: 25 · 2025: 84 · 2024: 101 · 2023: 105 · 2022: 123 · 2021: 121 · 2020: 84 · 2019: 13 · 2018: 2
- FWCI
- 27.79
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- 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)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- arXiv (Cornell University)
- Funders
- RWTH Aachen University
- H2020 European Research Council (773161)
- 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
- semi-automatic annotation, tracking dataset, convolutional neural networks, video frame
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