Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Abishek, M.
- A Behavioral Approach to Detect Somnolence of CAB Drivers Using Convolutional Neural Network
Abstract Views :244 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Sona College of Technology, IN
1 Department of Computer Science and Engineering, Sona College of Technology, IN
Source
ICTACT Journal on Soft Computing, Vol 11, No 3 (2021), Pagination: 2359-2363Abstract
The Road Traffic Accident Statistics concluded that fatal road accident 60% is caused by vehicle collision of taxi drivers. World Health Organization (WHO) is constantly initiating global road safety measures to minimize road accidents but, the cause for fatal injuries is primarily due to driving fatigue. Most people rely on cabs as the main transport. To provide obligate care of passengers, a computer vision-based technique is needed to detect the somnolence of drivers. Our proposed model CabSafety is based non-intrusive computer vision technique using Convolutional Neural Network (CNN). A tiny camera is fixed focusing the driver’s face to monitor the behavioral changes like an eye blink, yawing, watery eye, mouth movement, and head position. The measures of the driver’s eye are concentrated to identify sleepiness under stimulator or test conditions. The efficiency of the proposed model provides better results compared to the existing technique. The image from the camera is processed by OpenCV and Keras/Tensor flow. CNN classifier is used to detect eye status. The prediction from the CNN classifier produces an alarm to alert the driver.Keywords
Road Accident, Drowsy Driver, Eye Tracking, Convolutional Neural Network.
References
- R. PrietoCuriel, H. Gonzalez Ramirez and S.R. Bishop, “A Novel Rare Event Approach to Measure the Randomness and Concentration of Road accidents”, PloS One, Vol. 13, No. 8, pp. 1-12, 2018.
- A. Rossler, D. Cozzolino, L. Verdoliva and J. Thies, “Faceforensics: A Large-Scale Video Dataset for Forgery Detection in Human Faces”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 1-12, 2018.
- A.S. Zandi, A. Quddus and L. Prest, “Non-Intrusive Detection of Drowsy Driving based on Eye Tracking Data”, Transportation Research Record, Vol. 2673, No. 6, pp. 247-257, 2019.
- W. Deng and R. Wu, “Real-Time Driver-Drowsiness Detection System using Facial Features”, IEEE Access, Vol. 7, pp. 118727-118738, 2019.
- H.S. Stern, D. Blower and M.L. Cohen, “Data and Methods for Studying Commercial Motor Vehicle Driver Fatigue, Highway Safety and Long-Term Driver Health”, Accident Analysis and Prevention, Vol. 126, pp. 37-42, 2019.
- C. Schwarz, J. Gaspar and T. Miller, “The Detection of Drowsiness using a Driver Monitoring System”, Traffic Injury Prevention, Vol. 20, No. 2, pp. 157-161, 2019.
- M. Ramzan, H.U. Khan and S.M. Awan, “A Survey on State-of-the-Art Drowsiness Detection Techniques”, IEEE Access, Vol. 7, pp. 61904-61919, 2019.
- G.D. Finlayson, “Colour and Illumination in Computer Vision”, Interface Focus, Vol. 8, No. 4, pp. 1-12, 2018.
- J. Ma, X. Fan and S. Yang, “Contrast Limited Adaptive Histogram Equalization-Based Fusion in YIQ and HSI Color Spaces for Underwater Image Enhancement”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 32, No. 7, pp. 1-14, 2018.
- Y. Ji, S. Wang and Y. Zhao, “Eye and Mouth State Detection Algorithm based on Contour Feature Extraction”, Journal of Electronic Imaging, Vol. 27, No. 5, pp. 1-13, 2018.
- J. Ou and Y. Li, “Vector-Kernel Convolutional Neural Networks”, Neurocomputing, Vol. 330, pp. 253-258, 2019.
- A. Khan, A. Sohail and U. Zahoora, “A Survey of the Recent Architectures of Deep Convolutional Neural Networks”, Artificial Intelligence Review, Vol. 23, No. 1, pp. 1-16, 2020.
- C. J. Bourdin and J.L. Vercher, “Detection and Prediction of Driver Drowsiness using Artificial Neural Network Models”, Accident Analysis and Prevention, Vol. 126, No. 1, pp. 95-104, 2019.
- S. Sahu, A.K. Singh and S.P. Ghrera, “An Approach for De-Noising and Contrast Enhancement of Retinal Fundus Image using CLAHE”, Optics and Laser Technology, Vol. 110, pp. 87-98, 2019.
- V.B. Hemadri, P. Gundgurti and K. Deepika, “A Novel on Biometric Parameter’s Fusion on Drowsiness Detection using Machine Learning”, Proceedings of International Conference on Computer Networks and Communication Technologies, pp. 1-12, 2019.