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A Deep Learning Method for Automatic Visual Attention Detection in Older Drivers [r-libre/1748]

Chikhaoui, Belkacem; Ruer, Perrine, & Vallières, Évelyne F. (2019). A Deep Learning Method for Automatic Visual Attention Detection in Older Drivers. In How AI Impacts Urban Living and Public Health. ICOST 2019 (p. 49-60). New York City, USA : Springer, coll. « Lecture Notes in Computer Science », vol. 11862. https://doi.org/10.1007/978-3-030-32785-9_5

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  PDF - ICOST_2019 (5).pdf
Content : Accepted Version
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Item Type: Papers in Conference Proceedings
Refereed: Yes
Status: Published
Abstract: This paper addresses a new problem of automatic detection of visual attention in older adults based on their driving speed. All state-of-the-art methods try to understand the on-road performance of older adults by means of the Useful Field of View (UFOV) measure. Our method takes advantage of deep learning models such as Long-short Term Memory (LSTM) to automatically extract features from driving speed data for predicting drivers' visual attention. We demonstrate, through extensive experiments on real dataset, that our method is able to predict the driver's visual attention based on driving speed with high accuracy.
Official URL: https://link.springer.com/chapter/10.1007/978-3-03...
Depositor: Chikhaoui, Belkacem
Owner / Manager: Belkacem Chikhaoui
Deposited: 04 Sep 2019 17:21
Last Modified: 24 Nov 2021 19:51

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