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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/ .
Original language | English |
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Title of host publication | Computer Vision – ECCV 2014 |
Subtitle of host publication | 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V |
Publisher | Springer |
Pages | 361-376 |
Number of pages | 16 |
ISBN (Electronic) | 978-3-319-10602-1 |
ISBN (Print) | 978-3-319-10601-4 |
DOIs | |
Publication status | Published - 12 Sept 2014 |
Event | European Conference on Computer Vision 2014 - Zurich, Switzerland Duration: 5 Sept 2014 → 12 Sept 2014 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 8693 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | European Conference on Computer Vision 2014 |
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Abbreviated title | ECCV 2014 |
Country/Territory | Switzerland |
City | Zurich |
Period | 5/09/14 → 12/09/14 |
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Dive into the research topics of 'Training Object Class Detectors from Eye Tracking Data'. Together they form a unique fingerprint.Projects
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Profiles
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Frank Keller
- School of Informatics - Personal Chair in Computational Cognitive Science
- Institute of Language, Cognition and Computation
- Language, Interaction, and Robotics
Person: Academic: Research Active