19th June 2020, Seattle, WA, USA
In Conjunction with the Conference on Computer Vision and Pattern Recognition, CVPR 2020
Challenge Papers

Workshop Slides

Import Information!

  • Schedule and Keynote Speaker list can be found here

  • Thank you to all challenge participants. Requests for short papers and videos have now been sent out.
  • Paper and video submission deadline: June 11, 2020


We have 3 exciting challenges for the 5th edition of the BMTT MOTChallenge workshop!

  • MOTSChallenge: Multi-Object Tracking and Segmentation for crowded pedestrian scenes on the MOTS20 dataset (pedestrians only)
  • KITTI-MOTS: Multi-Object Tracking and Segmentation for road traffic scenarios on the KITTI MOTS dataset (cars and pedestrians)
  • Supplied masks: Multi-Object Tracking using the supplied segmentation masks on both the MOTS20 and the KITTI MOTS datasets (cars and pedestrians)

The authors of the strongest methods will be invited to submit a short workshop paper and to present their work during the workshop.

MOTS Challenges 2020

Challenge Details


For this 5th edition of our Benchmarking Multi-Target Tracking (MOTChallenge) Workshop, we want to push the limits of tracking by focusing on tracking at pixel accurate level. To achieve this we introduce the MOTS20 (Multi-Object Tracking and Segmentation 2020) benchmark. For this we have densely annotated 4 training sequences and 4 challenging test sequences from the challenging MOT17 dataset with pixel accurate segmentation masks. The data features crowded pedestrian scenes with many difficult cases of occlusion.

In order to enourage research towards end-to-end systems which solve the whole MOTS task together, for this challenge the partipants are not required to use "public detections", but can rather use any detector. We also include a challenge track where we provide state-of-the-art segmentation predictions and participants can focus on the tracking aspect without having to perform detection and segmentation.

We also host another challenge track on the KITTI-MOTS dataset, where methods are required to detect, segment and track both cars and people in an autonomous vehicle setting.

Our workshop has three major goals: (i) Analyzing the performance of state-of-the-art detection, tracking, and segmentation algorithms, and their combinations on crowded pedestrian scenes, (ii) opening new Multi-Object Tracking and Segmentation Challenges on 4 the MOTS20 and KITTI MOTS datasets, and (iii) discussing the limitations of current methods for producing accurate segmentation masks under severe occlusion scenarios.

An important part of the workshop will also be dedicated to a discussion among participants on how to improve Multi-Object Tracking and Segmentation evaluation and ideas on how to expand the current benchmark. These discussions in previous editions of the workshop have helped us tremendously in shaping MOTChallenge and significantly contributed to creating a widely used and perhaps the most popular multi-object tracking benchmark in the community.

For any questions, please contact Paul Voigtlaender

Participating institutions