  Standardized benchmarks are crucial for the majority of computer vision
applications. Although leaderboards and ranking tables should not be
over-claimed, benchmarks often provide the most objective measure of
performance and are therefore important guides for research. The benchmark for
Multiple Object Tracking, MOTChallenge, was launched with the goal to establish
a standardized evaluation of multiple object tracking methods. The challenge
focuses on multiple people tracking, since pedestrians are well studied in the
tracking community, and precise tracking and detection has high practical
relevance. Since the first release, MOT15, MOT16, and MOT17 have tremendously
contributed to the community by introducing a clean dataset and precise
framework to benchmark multi-object trackers. In this paper, we present our
MOT20benchmark, consisting of 8 new sequences depicting very crowded
challenging scenes. The benchmark was presented first at the 4thBMTT MOT
Challenge Workshop at the Computer Vision and Pattern Recognition Conference
(CVPR) 2019, and gives to chance to evaluate state-of-the-art methods for
multiple object tracking when handling extremely crowded scenarios.
