Humans disagree with the IoU for measuring object detector localization error

Autor:	Strafforello, Ombretta, Rajasekart, Vanathi, Kayhan, Osman S., Inel, Oana, van Gemert, Jan
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Human-Computer Interaction
Druh dokumentu:	Working Paper
Popis:	The localization quality of automatic object detectors is typically evaluated by the Intersection over Union (IoU) score. In this work, we show that humans have a different view on localization quality. To evaluate this, we conduct a survey with more than 70 participants. Results show that for localization errors with the exact same IoU score, humans might not consider that these errors are equal, and express a preference. Our work is the first to evaluate IoU with humans and makes it clear that relying on IoU scores alone to evaluate localization errors might not be sufficient. Comment: Published at ICIP 2022. Ombretta Strafforello, Vanathi Rajasekart, Osman S. Kayhan and Oana Inel contributed equally to this work
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2207.14221 Zobrazit plný text záznamu View this record from Arxiv