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Abishek, M.
- Advanced Eye Gaze Detector
Authors
Source
Automation and Autonomous Systems, Vol 8, No 7 (2016), Pagination: 209-212Abstract
This paper addresses the eye gaze tracking problem using a low-cost and more convenient web camera in a desktop environment, as opposed to gaze tracking techniques requiring specific hardware, e.g., infrared high-resolution camera and infrared light sources, as well as a cumbersome calibration process. In the proposed method, we first track the human face in a real-time video sequence to extract the eye regions. Then, we combine intensity energy and edge strength to obtain the iris centre and utilize the piecewise eye corner detector to detect the eye corner. We adopt a sinusoidal head model to simulate the 3-D head shape and propose adaptive weighted facial features embedded in the pose from the orthography and scaling with iterations algorithm, whereby the head pose can be estimated. Finally, the eye gaze tracking is accomplished by integration of the eye vector and the head movement information. Experiments are performed to estimate the eye movement and head pose on the BioID dataset and pose Dataset, respectively. In addition, experiments for gaze tracking are performed in real-time video sequences under a desktop environment. The proposed method is not sensitive to the light conditions. Experimental results show that our method achieves an average accuracy of around 1.28◦ without head movement and 2.27◦ with the minor movement of the head.
- A Behavioral Approach to Detect Somnolence of CAB Drivers Using Convolutional Neural Network
Authors
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
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