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.
The Multiple Object Tracking Accuracy (MOTA) metric is the most commonly used metric for well established tracking challenges. MOTA is defined on a per frame basis. For each frame @math , let @math be the number of misses, @math be the number of false positives, @math represents the number of ID switches, and @math be the number of true detections for frame @math . Define [ MOTA = 1 - t ( m_t + fp_t + ) g_t ] .2in The Hungarian algorithm @cite_0 is used to match ground-truth boxes to system boxes for each frame. A system track stays assigned to the same ground-truth track, unless the IoU IoU stands for Intersection over Union. Often, detectors or trackers apply a threshold for correct detections such as @math . drops below 0.5 for the current frame.
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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.