Subjective Annotations for Vision-Based Attention Level Estimation

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

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Abstrakt

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.

OriginalsprogEngelsk
TitelProceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications : Volume 5: VISAPP
RedaktørerAndreas Kerren, Christophe Hurter, Jose Braz
Antal sider8
Vol/bind5
ForlagSCITEPRESS Digital Library
Publikationsdato2019
Sider249-256
ISBN (Trykt)978-989-758-354-4
ISBN (Elektronisk)9789897583544
DOI
StatusUdgivet - 2019
Begivenhed14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Visigrapp 2019) - Prague, Tjekkiet
Varighed: 25 feb. 201927 feb. 2019

Konference

Konference14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (Visigrapp 2019)
LandTjekkiet
ByPrague
Periode25/02/201927/02/2019

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  • Citationsformater

    Coifman, A. L., Rohoska, P., Kristoffersen, M. S., Shepstone, S. E., & Tan, Z-H. (2019). Subjective Annotations for Vision-Based Attention Level Estimation. I A. Kerren, C. Hurter, & J. Braz (red.), Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: Volume 5: VISAPP (Bind 5, s. 249-256). SCITEPRESS Digital Library. https://doi.org/10.5220/0007311402490256