  Evaluating the performance of multi-object tracking (MOT) methods is not
straightforward, and existing performance measures fail to consider all the
available uncertainty information in the MOT context. This can lead
practitioners to select models which produce uncertainty estimates of lower
quality, negatively impacting any downstream systems that rely on them.
Additionally, most MOT performance measures have hyperparameters, which makes
comparisons of different trackers less straightforward. We propose the use of
the negative log-likelihood (NLL) of the multi-object posterior given the set
of ground-truth objects as a performance measure. This measure takes into
account all available uncertainty information in a sound mathematical manner
without hyperparameters. We provide efficient algorithms for approximating the
computation of the NLL for several common MOT algorithms, show that in some
cases it decomposes and approximates the widely-used GOSPA metric, and provide
several illustrative examples highlighting the advantages of the NLL in
comparison to other MOT performance measures.
