Publication Type : Conference Paper
Publisher : Springer Singapore
Source : Computer Networks and Inventive Communication Technologies: Proceedings of Third ICCNCT 2020 Pages 407-415, 2021
Url : https://link.springer.com/chapter/10.1007/978-981-15-9647-6_31
Campus : Amritapuri
School : School of Engineering
Department : Electronics and Communication
Year : 2021
Abstract : Forewarning drowsy drivers can reduce the number of road accidents. A non-intrusive drowsiness detection system is implemented, which alerts the driver on the onset of drowsiness. A Pi camera module attached to Raspberry Pi is used to acquire and process the live video of the driver. Haar face detector in OpenCV is used for face detection followed by 68 points of facial landmark identification. Eye and Mouth Aspect Ratios, blink rate and yawning rate are the features extracted. Drowsiness detection is done using two methodologies viz. a threshold-based one and the other, employing artificial intelligence. The machine learning techniques used are LDA and SVM. Feedback is provided as an alarm if a driver is found to be drowsy. The analysis shows that machine learning-based techniques viz. LDA and SVM outperform threshold technique for the dataset considered.
Cite this Research Publication : Madireddy, R. et al. (2021). Driver Drowsiness Detection System Using Conventional Machine Learning. In: Smys, S., Palanisamy, R., Rocha, Á., Beligiannis, G.N. (eds) Computer Networks and Inventive Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 58. Springer, Singapore. https://doi.org/10.1007/978-981-15-9647-6_31