What Humans Consider Good Object Detection

Analysis on how automatic object detectors align with what humans consider good object detection

Master thesis (2021)

Authors

V.S. Rajasekar Electrical Engineering, Mathematics and Computer Science

Contributors

J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (supervisor 1)
A.J. van Genderen Computer Engineering - EEMCS (supervisor 2)
S. Pintea Pattern Recognition and Bioinformatics - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Vanathi Rajasekar
More Info
expand_more

Published Date

26-03-2021

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

How do automatic object detector outputs align with what humans consider good object detection? Our study is based on the responses of 70 participants for a survey. The participants are presented with images having bound- ing box predictions, their task is to choose images which according to them have an acceptable or a good detection. The results show a correlation between the size of the object and the evaluation metric IoU (Intersection over Union), with the size of the bounding box. Furthermore, the data indicates that the kind of box they prefer most for a detection output, is also the most accepted detection by them. Additionally, the results suggest that based on the symmetry of the object, position of the bounding box may or may not play a role for considering a detection valid. Our study investigates through human subjective choices if the traditional threshold value of IoU for evaluation, and tight bounding box outputs are always the best outputs in object detection techniques.