Advanced Technology Labs Israel, Microsoft Research, Microsoft R&D Center, Matam Park, Haifa, Israel.
IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2553-60. doi: 10.1109/TPAMI.2013.70.
There exists an abundance of systems and algorithms for multiple target detection and tracking in video, and many measures for evaluating the quality of their output have been proposed. The contribution of this paper lies in the following: first, it argues that such performance measures should have two fundamental properties--monotonicity and error type differentiability; second, it shows that the recently proposed measures do not have either of these properties and are, thus, less usable; third, it composes a set of simple measures, partly built on common practice, that does have these properties. The informativeness of the proposed set of performance measures is demonstrated through their application on face detection and tracking results.
在视频中的多目标检测和跟踪方面存在着大量的系统和算法，并且已经提出了许多评估其输出质量的度量方法。本文的贡献在于：首先，它认为这些性能度量应该具有两个基本属性——单调性和误差类型可区分性；其次，它表明最近提出的度量方法不具有这两个属性，因此不太可用；第三，它组成了一组简单的度量方法，部分基于常见的实践，具有这些属性。通过在人脸检测和跟踪结果上的应用，展示了所提出的性能度量集的信息性。
