AggreGaze: Collective Estimation of Audience Attention on Public Displays
Yusuke Sugano, Xucong Zhang, Andreas Bulling
Proceedings of the ACM Symposium on User Interface Software and Technology (UIST), pp. 821–831, 2016.
Abstract
Gaze is frequently explored in public display research given its importance for monitoring and analysing audience attention. However, current gaze-enabled public display interfaces require either special-purpose eye tracking equipment or explicit personal calibration for each individual user. We present AggreGaze, a novel method for estimating spatio-temporal audience attention on public displays. Our method requires only a single off-the-shelf camera attached to the display, does not require any personal calibration, and provides visual attention estimates across the full display. We achieve this by 1) compensating for errors of state-of-the-art appearance-based gaze estimation methods through on-site training data collection, and by 2) aggregating uncalibrated and thus inaccurate gaze estimates of multiple users into joint attention estimates. We propose different visual stimuli for this compensation: a standard 9-point calibration, moving targets, text and visual stimuli embedded into the display content, as well as normal video content. Based on a two-week deployment in a public space, we demonstrate the effectiveness of our method for estimating attention maps that closely resemble ground-truth audience gaze distributions.Links
BibTeX
@inproceedings{sugano16_uist,
title = {AggreGaze: Collective Estimation of Audience Attention on Public Displays},
author = {Sugano, Yusuke and Zhang, Xucong and Bulling, Andreas},
year = {2016},
booktitle = {Proceedings of the ACM Symposium on User Interface Software and Technology (UIST)},
pages = {821--831},
doi = {10.1145/2984511.2984536}
}