A unified metric is given for the evaluation of 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). Preliminary scoring results are given on a standard tracking dataset (Oxford Town Centre Dataset). In the final section, prospective advantages of the metric are listed, along with ideas for improving the metric. We end with a couple of open questions concerning tracking metrics.
This paper has always been one of my favorite “children,” combining as it does elements of the duality of linear programming and combinatorial tools from graph theory. It may be of some interest to tell the story of its origin.