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Authors: Andrea Coifman 1 ; Péter Rohoska 2 ; Miklas Kristoffersen 3 ; Sven Shepstone 4 and Zheng-Hua Tan 1

Affiliations: 1 Department of Electronic Systems, Aalborg University, 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, Denmark ; 4 Bang & Olufsen A/S, Struer, Denmark

ISBN: 978-989-758-354-4

Keyword(s): Attention Level Estimation, Natural HCI, Human Behavior Analysis, Subjective Annotations.

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. (More)

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Paper citation in several formats:
Coifman, A.; Rohoska, P.; Kristoffersen, M.; Shepstone, S. and Tan, Z. (2019). Subjective Annotations for Vision-based Attention Level Estimation.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 249-256. DOI: 10.5220/0007311402490256

@conference{visapp19,
author={Andrea Coifman. and Péter Rohoska. and Miklas S. Kristoffersen. and Sven E. Shepstone. and Zheng{-}Hua Tan.},
title={Subjective Annotations for Vision-based Attention Level Estimation},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={249-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007311402490256},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Subjective Annotations for Vision-based Attention Level Estimation
SN - 978-989-758-354-4
AU - Coifman, A.
AU - Rohoska, P.
AU - Kristoffersen, M.
AU - Shepstone, S.
AU - Tan, Z.
PY - 2019
SP - 249
EP - 256
DO - 10.5220/0007311402490256

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