Monotonicity and Error Type Differentiability in Performance Measures for Target Detection and Tracking in Video
Issue No. 10 - Oct. (2013 vol. 35)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.70
I. Leichter , Adv. Technol. Labs. Israel- Microsoft Res., Microsoft R&D Center, Haifa, Israel
E. Krupka , Adv. Technol. Labs. Israel- Microsoft Res., Microsoft R&D Center, Haifa, Israel
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.
Target tracking, Measurement uncertainty, Corporate acquisitions, Indexes, Object detection, Context
I. Leichter and E. Krupka, "Monotonicity and Error Type Differentiability in Performance Measures for Target Detection and Tracking in Video," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. 10, pp. 2553-2560, 2013.