MOTS: Multi-Object Tracking and Segmentation

2019

Conference Paper

avg

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.

@inproceedings{Voigtlaender2019CVPR,
  title = {MOTS: Multi-Object Tracking and Segmentation},
  author = {Voigtlaender, Paul and Krause, Michael and Osep, Aljosa and Luiten, Jonathon and Sekar, Berin Balachandar Gnana and Geiger, Andreas and Leibe, Bastian},
  booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)},
  month = jun,
  year = {2019},
  doi = {},
  month_numeric = {6}
}