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Real-Time Driver Fatigue Detection Based on ELM

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Proceedings of ELM-2015 Volume 2

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 7))

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Abstract

Driver fatigue is a serious road safety issue that results in thousands of road crashes every year. Image-based fatigue monitoring is one of the most important methods of avoiding fatigue-related accidents. In this paper, a vision-based real-time driver fatigue detection system based on ELM is proposed. The system has three main stages. The first stage performs facial features localization and tracking, by using the Viola–Jones face detector and the KLT algorithm. The second stage is the judgement of facial and fatigue status, applying twice ELM with an extremely fast learning speed. The last one is online learning, which can continuously improve ELM accuracy according to the user’s feedback. Multiple facial features (including the movement of eyes, head and mouth) are used to comprehensively assess the driver vigilance state. In comparison to backpropagation (BP), the experimental results showed that applying ELM has a better performance with much faster training speed.

The work is partially supported by the National Narural Science Foundation of China (No. 61272180, 61202086, 61272179, 61472071) and the Fundamental Research Funds for the Central Universities (No. N140404013).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China

    Hengyu Liu, Tiancheng Zhang, Haibin Xie, Hongbiao Chen & Fangfang Li

Authors
  1. Hengyu Liu
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  2. Tiancheng Zhang
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  3. Haibin Xie
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  4. Hongbiao Chen
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  5. Fangfang Li
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Corresponding author

Correspondence to Tiancheng Zhang .

Editor information

Editors and Affiliations

  1. Institute of Information and Contro, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Nanyang Technological University, Singapore, Singapore

    Kezhi Mao

  3. ECE, U of Windsor, WINDSOR, Ontario, Canada

    Jonathan Wu

  4. Dept of Mechanical and Industrial Engg, University of Iowa, Iowa City, Iowa, USA

    Amaury Lendasse

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Liu, H., Zhang, T., Xie, H., Chen, H., Li, F. (2016). Real-Time Driver Fatigue Detection Based on ELM. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_36

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  • DOI: https://doi.org/10.1007/978-3-319-28373-9_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28372-2

  • Online ISBN: 978-3-319-28373-9

  • eBook Packages: EngineeringEngineering (R0)

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