Abstract
Eye tracking is nowadays the primary method for collecting training data for neural networks in the Human Visual System modelling. Our recommendation is to collect eye tracking data from videos with eye tracking glasses that are more affordable and applicable to diverse test conditions than conventionally used screen based eye trackers. Eye tracking glasses are prone to moving during the gaze data collection but our experiments show that the observed displacement error accumulates fairly linearly and can be compensated automatically by the proposed framework. This paper describes how our framework can be used in practice with videos up to 4K resolution. The proposed framework and the data collected during our sample experiment are made publicly available.
Original language | English |
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Title of host publication | 2018 IEEE International Symposium on Multimedia (ISM) |
Publisher | IEEE |
Pages | 299-302 |
Number of pages | 4 |
ISBN (Electronic) | 978-1-5386-6857-3 |
ISBN (Print) | 978-1-5386-6858-0 |
DOIs | |
Publication status | Published - Dec 2018 |
Publication type | A4 Article in conference proceedings |
Event | IEEE International Symposium on Multimedia - Taichung, Taiwan, Province of China Duration: 10 Dec 2018 → 12 Dec 2018 Conference number: 20 |
Conference
Conference | IEEE International Symposium on Multimedia |
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Abbreviated title | ISM |
Country/Territory | Taiwan, Province of China |
City | Taichung |
Period | 10/12/18 → 12/12/18 |
Keywords
- eye tracking
- open-source
- open data set
- error correction
Publication forum classification
- Publication forum level 1