@inproceedings{bf31c1e3ebb9489f93440f2f9d451933,
title = "An Eye-Tracking Database of Video Advertising",
abstract = "Reliably predicting where people look in images and videos remains challenging and requires substantial eye-tracking data to be collected and analysed for various applications. In this paper, we present an eye-tracking study where twenty-eight participants viewed forty still scenes of video advertising. First, we analyse human attentional behaviour based on gaze data. Then, we evaluate to what extent a machine - saliency model - can predict human behaviour. Experimental results show that there is a significant gap between human and machine in visual saliency. The resulting eye-tracking data would benefit the development of saliency models for video advertising or other relevant applications. The eye-tracking data are made publicly available to the research community.",
keywords = "Eye-tracking, saliency, video advertising, visual attention",
author = "Lucie Leveque and Hantao Liu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 26th IEEE International Conference on Image Processing, ICIP 2019 ; Conference date: 22-09-2019 Through 25-09-2019",
year = "2019",
month = sep,
doi = "10.1109/ICIP.2019.8802989",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "425--429",
booktitle = "2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings",
}