Subjective Annotations for Vision-based Attention Level Estimation

Andrea Coifman, Péter Rohoska, Miklas Kristoffersen, Sven Shepstone, Zheng-Hua Tan

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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Paper Citation


in Harvard Style

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


in Bibtex Style

@conference{visapp19,
author={Andrea Coifman and Péter Rohoska and Miklas Kristoffersen and Sven 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},
}


in EndNote Style

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