Optimal matching problem in detection and recognition performance evaluation

摘要

This paper proposes a principle of one-to-one correspondence in performance evaluation of a general class of detection and recognition algorithms. Such a correspondence between ground-truth entities and algorithm declared entities is essential in accurately computing objective performance measures such as the detection, recognition, and false alarm rates. We mathematically define the correspondence by formulating a combinatorial optimal matching problem. In addition to evaluating detection performance, this methodology is also capable of evaluating recognition performance. Our study shows that the proposed principle for detection performance evaluation is simple, general and mathematically sound. The derived performance evaluation technique is widely applicable, precise, consistent and efficient.