Eye Tracking for Everyone
Name
Matusik_Eye tracking.pdf
Size
6.74 MB
Format
Adobe PDF
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Author(s)
Kellnhofer, Petr
Bhandarkar, Suchendra
Khosla, Aditya
Kannan, Harini D.
Matusik, Wojciech
Torralba, Antonio
Date Issued
December 2016
Journal
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Krafka, Kyle et al. “Eye Tracking for Everyone.” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30 2016, Las Vegas, Neveda, USA, Institute of Electrical and Electronics Engineers (IEEE), December 2016: 2176-2184 © 2016 Institute of Electrical and Electronics Engineers (IEEE)
Version
Author's final manuscript
Abstract
From scientific research to commercial applications, eye tracking is an important tool across many domains. Despite its range of applications, eye tracking has yet to become a pervasive technology. We believe that we can put the power of eye tracking in everyone's palm by building eye tracking software that works on commodity hardware such as mobile phones and tablets, without the need for additional sensors or devices. We tackle this problem by introducing GazeCapture, the first large-scale dataset for eye tracking, containing data from over 1450 people consisting of almost 2:5M frames. Using GazeCapture, we train iTracker, a convolutional neural network for eye tracking, which achieves a significant reduction in error over previous approaches while running in real time (10-15fps) on a modern mobile device. Our model achieves a prediction error of 1.71cm and 2.53cm without calibration on mobile phones and tablets respectively. With calibration, this is reduced to 1.34cm and 2.12cm. Further, we demonstrate that the features learned by iTracker generalize well to other datasets, achieving state-of-the-art results.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2016.239
