PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Inferring Object Relevance from Gaze in Dynamic Scenes
Melih Kandemir, Veli-Matti Saarinen and Samuel Kaski
In: Proceedings of ETRA 2010, ACM Symposium on Eye Tracking Research & Applications, Austin, TX, USA, March 22-24 (2010) ACM , New York, NY , pp. 105-108.

Abstract

As prototypes of data glasses having both data augmentation and gaze tracking capabilities are becoming available, it is now possible to develop proactive gaze-controlled user interfaces to display information about objects, people, and other entities in real-world setups. In order to decide which objects the augmented information should be about, and how saliently to augment, the system needs an estimate of the importance or relevance of the objects of the scene for the user at a given time. The estimates will be used to minimize distraction of the user, and for providing efficient spatial management of the augmented items. This work is a feasibility study on inferring the relevance of objects in dynamic scenes from gaze. We collected gaze data from subjects watching a video for a pre-defined task. The results show that a simple ordinal logistic regression model gives relevance rankings of scene objects with a promising accuracy.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
ID Code:7593
Deposited By:Samuel Kaski
Deposited On:17 March 2011