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Automatic Detection of a Driver’s Complex Mental States

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10406))

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Abstract

Automatic classification of drivers’ mental states is an important yet relatively unexplored topic. In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We present our video segmentation and annotation methodology of a spontaneous dataset of natural driving videos from 10 different drivers. We also present our real-time annotation tool used for labelling the dataset via an emotion perception experiment and discuss the challenges faced in obtaining the ground truth labels. Finally, we present a methodology for automatic classification of drivers’ mental states. We compare SVM models trained on our dataset with an existing nearest neighbour model pre-trained on posed dataset, using facial Action Units as input features. We demonstrate that our temporal SVM approach yields better results. The dataset’s extracted features and validated emotion labels, together with the annotation tool, will be made available to the research community.

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Notes

  1. 1.

    Please email marwa.mahmoud@cl.cam.ac.uk for the link and password.

  2. 2.

    https://github.com/mzy0369/VideoAnnotator.

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Acknowledgment

The work presented in this paper was funded and supported by Jaguar Land Rover, Coventry, UK.

Author information

Authors and Affiliations

  1. Department of Engineering, University of Cambridge, Cambridge, UK

    Zhiyi Ma

  2. Computer Laboratory, University of Cambridge, Cambridge, UK

    Marwa Mahmoud & Peter Robinson

  3. Jaguar Land Rover, Coventry, UK

    Eduardo Dias & Lee Skrypchuk

Authors
  1. Zhiyi Ma
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  2. Marwa Mahmoud
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  3. Peter Robinson
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  4. Eduardo Dias
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  5. Lee Skrypchuk
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Corresponding author

Correspondence to Zhiyi Ma .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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Ma, Z., Mahmoud, M., Robinson, P., Dias, E., Skrypchuk, L. (2017). Automatic Detection of a Driver’s Complex Mental States. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10406. Springer, Cham. https://doi.org/10.1007/978-3-319-62398-6_48

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  • DOI: https://doi.org/10.1007/978-3-319-62398-6_48

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  • Print ISBN: 978-3-319-62397-9

  • Online ISBN: 978-3-319-62398-6

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