  A unified metric is given for the evaluation of object tracking systems. The
metric is inspired by KL-divergence or relative entropy, which is commonly used
to evaluate clustering techniques. Since tracking problems are fundamentally
different from clustering, the components of KL-divergence are recast to handle
various types of tracking errors (i.e., false alarms, missed detections,
merges, splits). Scoring results are given on a standard tracking dataset
(Oxford Town Centre Dataset), as well as several simulated scenarios. Also,
this new metric is compared with several other metrics including the commonly
used Multiple Object Tracking Accuracy metric. In the final section, advantages
of this metric are given including the fact that it is continuous,
parameter-less and comprehensive.
