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- Title
A BEHAVIORAL APPROACH TO DETECT SOMNOLENCE OF CAB DRIVERS USING CONVOLUTIONAL NEURAL NETWORK.
- Authors
Vinodhini, V.; Abishek, M.; Divya, K.
- Abstract
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 visionbased 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.
- Subjects
WORLD Health Organization; CONVOLUTIONAL neural networks; TRAFFIC accident statistics; TAXICAB drivers; ROAD safety measures; EYE movements; DROWSINESS
- Publication
ICTACT Journal on Soft Computing, 2021, Vol 11, Issue 3, p2359
- ISSN
0976-6561
- Publication type
Article
- DOI
10.21917/ijsc.2021.0337