Towards a more effective method for analyzing mobile eye-tracking data: integrating gaze data with object recognition algorithms

Abstract

In this paper we present the outlines of a new project that aims at developing and implementing effective new methods for analyzing gaze data collected with mobile eye-tracking devices. More specifically, we argue for the integration of object recognition algorithms from vision engineering, such as invariant region matching techniques, in gaze analysis software. We present a series of arguments why an object-based approach may provide a significant surplus, in terms of analytical precision, flexibility, additional application areas and cost efficiency, to the existing systems that use predefined areas of analysis. In order to test the actual analytical power of object recognition algorithms for the analysis of gaze data recorded in the wild, we develop a series of test cases in different real world situations, including shopping behavior, navigation, handling and usability of mobile systems. By setting up these case studies in close collaboration with key players in the relevant fields (retailers, signage consultants, market and user-experience research, and developers of eye-tracking hard- and software), we will be able to sketch an accurate picture of the pros and cons of the proposed method in comparison to current analytical practice.