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FPGA Based Human Fatigue and Drowsiness Detection System Using Deep Neural Network for Vehicle Drivers in Road Accident Avoidance System

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Human Behaviour Analysis Using Intelligent Systems

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 6))

Abstract

Automobile Industry shares numerous accidents in our daily routine. Increasing rate of road accidents are due to driver distraction such as fatigue and lack of sleep. This work is intended solely for the implementation of fatigue and drowsiness detection system using the deep neural network in FPGA. In the proposed system, the image is preprocessed using median filtering and Viola Jones face detection algorithm for extracting the faces. Further, the features are extracted by using Local Binary Pattern analysis and the Max pooling is used to reduce the complexity level. These deep learning steps are followed by performing SVM classifier to define the status of the subject as drowsy or not. The system uses a camera to capture the real time image frames in addition with offline images of the system. The developed Vision-based driver fatigue and drowsiness detection system is a convenient technique for real time monitoring of driver’s vigilance.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, 626005, Tamilnadu, India

    D. Selvathi

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  1. D. Selvathi
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Correspondence to D. Selvathi .

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Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

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Selvathi, D. (2020). FPGA Based Human Fatigue and Drowsiness Detection System Using Deep Neural Network for Vehicle Drivers in Road Accident Avoidance System. In: Hemanth, D. (eds) Human Behaviour Analysis Using Intelligent Systems. Learning and Analytics in Intelligent Systems, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-35139-7_4

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