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2019 | OriginalPaper | Chapter

Verification of Identification Accuracy of Eye-Gaze Data on Driving Video

Authors : Naoto Mukai, Kazuhiro Fujikake, Takahiro Tanaka, Hitoshi Kanamori

Published in: Intelligent Interactive Multimedia Systems and Services

Publisher: Springer International Publishing

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Abstract

It is said that the most cause of traffic accidents is the lack of confirming the safety. Visual information from both eyes is one of the important factors for safe driving. In this paper, we collect eye-gaze data of drivers who watch a driving video, and try to develop a model of their eye movements to identify factors to enhance their safety. For the purpose of modeling, we adopted a recurrent neural network and Long Short-Term Memory (LSTM) to the collected eye-gaze data because the LSTM is able to deal with a time-series data such as the eye-gaze data. Moreover, we performed an experiment to evaluate the identification accuracy of drivers. The results indicated that the driver’s intention and habit can be approximated partially by the trained network, but it was insufficient to identify a personal driver for practical use.

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Footnotes
1
The report is publicly available at https://​www.​e-stat.​go.​jp/​.
 
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Metadata
Title
Verification of Identification Accuracy of Eye-Gaze Data on Driving Video
Authors
Naoto Mukai
Kazuhiro Fujikake
Takahiro Tanaka
Hitoshi Kanamori
Copyright Year
2019
Book
Intelligent Interactive Multimedia Systems and Services

Print ISBN: 978-3-319-92230-0

Electronic ISBN: 978-3-319-92231-7

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-319-92231-7_12

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