Abstract
Measuring audience attention towards pervasive displays is important but accurate measurement in real time remains a significant sensing challenge. Consequently, researchers and practitioners typically use other features, such as face presence, as a proxy. We provide a principled comparison of the performance of six features and their combinations for measuring attention: face presence, movement trajectory, walking speed, shoulder orientation, head pose, and gaze direction. We implemented a prototype that is capable of capturing this rich set of features from video and depth camera data. Using a controlled lab experiment (N=18) we show that as a single feature, face presence is indeed among the most accurate. We further show that accuracy can be increased through a combination of features (+10.3%), knowledge about the audience (+63.8%), as well as user identities (+69.0%). Our findings are valuable for display providers who want to collect data on display effectiveness or build interactive, responsive apps.
Dokumententyp: | Konferenzbeitrag (Paper) |
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Fakultät: | Mathematik, Informatik und Statistik > Informatik |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik |
Ort: | New York |
Sprache: | Englisch |
Dokumenten ID: | 47295 |
Datum der Veröffentlichung auf Open Access LMU: | 27. Apr. 2018, 08:12 |
Letzte Änderungen: | 13. Aug. 2024, 12:53 |