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01-Applied Mathematics & Information Sciences
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Volumes > Volume 17 > No. 5

 
   

Deep CNN Models-Based Ensemble Approach To Driver Drowsiness Detection

PP: 1050-1059
doi:10.18576/amis/170561
Author(s)
S Lavanya, S Praveen, K Bavithran2, G Muralitharan, S Naveen,
Abstract
Driver drowsiness has currently been a severe issue threatening road safety, hence it is vital to develop an effective drowsiness recognition algorithm to avoid traffic accidents. However, recognizing drowsiness is still very challenging, due to the large intra-class variations in facial expression, head pose and illumination condition. In this paper, a new deep learning framework based on hybrid Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. A real-time driver drowsiness detection and traffic analysis system for driving safety. Based on computer vision techniques, the driver's face is located in a colour video captured in acar. Then, face detection is employed to locate the regions of the driver's eyes, which are used as templates for eye tracking in subsequent frames. Finally, the tracked eye images are used for drowsiness detection to generate warning alarms. Also, the Camera capture image in front of the road, and analysis road traffics.

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