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Erschienen in:
2016 | OriginalPaper | Buchkapitel
Real-Time Driver Fatigue Detection Based on ELM
verfasst von : Hengyu Liu, Tiancheng Zhang, Haibin Xie, Hongbiao Chen, Fangfang Li
Erschienen in: Proceedings of ELM-2015 Volume 2
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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.