Authors:
Andrea Coifman
1
;
Péter Rohoska
2
;
Miklas S. Kristoffersen
3
;
Sven E. Shepstone
4
and
Zheng-Hua Tan
1
Affiliations:
1
Department of Electronic Systems, Aalborg University and Denmark
;
2
Department of Electronic Systems, Aalborg University, Denmark, Continental Automotive, Budapest and Hungary
;
3
Department of Electronic Systems, Aalborg University, Denmark, Bang & Olufsen A/S, Struer and Denmark
;
4
Bang & Olufsen A/S, Struer and Denmark
Keyword(s):
Attention Level Estimation, Natural HCI, Human Behavior Analysis, Subjective Annotations.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Features Extraction
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
Abstract:
Attention level estimation systems have a high potential in many use cases, such as human-robot interaction,
driver modeling and smart home systems, since being able to measure a person’s attention level opens the
possibility to natural interaction between humans and computers. The topic of estimating a human’s visual
focus of attention has been actively addressed recently in the field of HCI. However, most of these previous
works do not consider attention as a subjective, cognitive attentive state. New research within the field also
faces the problem of the lack of annotated datasets regarding attention level in a certain context. The novelty
of our work is two-fold: First, we introduce a new annotation framework that tackles the subjective nature
of attention level and use it to annotate more than 100,000 images with three attention levels and second,
we introduce a novel method to estimate attention levels, relying purely on extracted geometric features from
RGB and depth
images, and evaluate it with a deep learning fusion framework. The system achieves an overall
accuracy of 80.02%. Our framework and attention level annotations are made publicly available.
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