  There exists no comprehensive metric for describing the complexity of
Multi-Object Tracking (MOT) sequences. This lack of metrics decreases
explainability, complicates comparison of datasets, and reduces the
conversation on tracker performance to a matter of leader board position. As a
remedy, we present the novel MOT dataset complexity metric (MOTCOM), which is a
combination of three sub-metrics inspired by key problems in MOT: occlusion,
erratic motion, and visual similarity. The insights of MOTCOM can open nuanced
discussions on tracker performance and may lead to a wider acknowledgement of
novel contributions developed for either less known datasets or those aimed at
solving sub-problems. We evaluate MOTCOM on the comprehensive MOT17, MOT20, and
MOTSynth datasets and show that MOTCOM is far better at describing the
complexity of MOT sequences compared to the conventional density and number of
tracks. Project page at https://vap.aau.dk/motcom
