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A unified metric is given for the evaluation of object tracking systems. Themetric is inspired by KL-divergence or relative entropy, which is commonly usedto evaluate clustering techniques. Since tracking problems are fundamentallydifferent from clustering, the components of KL-divergence are recast to handlevarious 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 commonlyused Multiple Object Tracking Accuracy metric. In the final section, advantagesof this metric are given including the fact that it is continuous,parameter-less and comprehensive.

A Continuous, Full-scope, Spatio-temporal Tracking Metric based on KL-divergence