  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. We present a
benchmark for Multiple Object Tracking launched in the late 2014, with the goal
of creating a framework for the standardized evaluation of multiple object
tracking methods. This paper collects the two releases of the benchmark made so
far, and provides an in-depth analysis of almost 50 state-of-the-art trackers
that were tested on over 11000 frames. We show the current trends and
weaknesses of multiple people tracking methods, and provide pointers of what
researchers should be focusing on to push the field forward.
