Papers › MOTS: Multi-Object Tracking and Segmentation
MOTS: Multi-Object Tracking and Segmentation
Paul Voigtlaender, Michael Krause, Aljosa Osep, Jonathon Luiten, Berin Balachandar Gnana Sekar, Andreas Geiger, Bastian Leibe
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Object Tracking | MOTS20 | Track R-CNN | IDF1 | 42.4 | #6 of 6 | Archive leaderboard | report |
| Multi-Object Tracking | MOTS20 | Track R-CNN | sMOTSA | 40.6 | #6 of 6 | Archive leaderboard | report |
| Multiple Object Tracking | KITTI Test (Online Methods) | MOSTFusion | MOTA | 84.83 | #21 of 34 | Archive leaderboard | report |
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