Authors:
Stijn De Beugher
;
Geert Brône
and
Toon Goedemé
Affiliation:
KU Leuven, Belgium
Keyword(s):
Mobile Eye-tracking, Object Detection, Object Recognition, Real World, Data Analysis, Person Detection.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Pervasive Smart Cameras
Abstract:
In this paper we present a novel method for the automatic analysis of mobile eye-tracking data in natural environments. Mobile eye-trackers generate large amounts of data, making manual analysis very time-consuming. Available solutions, such as marker-based analysis minimize the manual labour but require experimental control, making real-life experiments practically unfeasible. We present a novel method for processing this mobile eye-tracking data by applying object, face and person detection algorithms. Furthermore we present a temporal smoothing technique to improve the detection rate and we trained a new detection model for occluded person and face detections. This enables the analysis to be performed on the object level rather than the traditionally used coordinate level. We present speed and accuracy results of our novel detection scheme on challenging, large-scale real-life experiments.