Abstract
Current eye tracking technologies have a number of drawbacks when it comes to practical use in real-world settings. Common challenges, such as high levels of daylight, eyewear (eg spectacles or contact lenses) and eye make-up, give rise to noise that undermines their utility as a standard component for mobile computing, design, and evaluation. To work around these challenges, we introduce CrowdEyes, a mobile eye tracking solution that utilizes crowdsourcing for increased tracking accuracy and robustness. We present a pupil detection task design for crowd workers together with a study that demonstrates the high-level accuracy of crowdsourced pupil detection in comparison to state-of-the-art pupil detection algorithms. We further demonstrate the utility of our crowdsourced analysis pipeline in a fixation tagging task. In this paper, we validate the accuracy and robustness of harnessing the crowd as both an …
Original language | English |
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Pages | 18 |
Number of pages | 10 |
Publication status | Published - 2017 |
Event | Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services: MobileHCI - Duration: 4 Sept 2017 → … |
Conference
Conference | Proceedings of the 19th International Conference on Human-Computer Interaction with Mobile Devices and Services |
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Period | 4/09/17 → … |
Research Groups and Themes
- Digital Health
Keywords
- crowdsourcing
- Eye-tracking
- HCI