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Training Object Class Detectors from Eye Tracking Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2014
Subtitle of host publication13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V
PublisherSpringer International Publishing
Pages361-376
Number of pages16
ISBN (Electronic)978-3-319-10602-1
ISBN (Print)978-3-319-10601-4
DOIs
Publication statusPublished - Sep 2014

Publication series

NameLecture Notes in Computer Science
Volume8693
ISSN (Print)0302-9743

Abstract

Training an object class detector typically requires a large set of images annotated with bounding-boxes, which is expensive and time consuming to create. We propose novel approach to annotate object locations which can substantially reduce annotation time. We first track the eye movements of annotators instructed to find the object and then propose a technique for deriving object bounding-boxes from these fixations. To validate our idea, we collected eye tracking data for the trainval part of 10 object classes of Pascal VOC 2012 (6,270 images, 5 observers). Our technique correctly produces bounding-boxes in 50%of the images, while reducing the total annotation time by factor 6.8× compared to drawing bounding-boxes. Any standard object class detector can be trained on the bounding-boxes predicted by our model. Our large scale eye tracking dataset is available at groups.inf.ed.ac.uk/calvin/eyetrackdataset/ .

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