  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.
