An Efficient Framework to Detect and Avoid Driver Sleepiness Based on YOLO with Haar Cascades and an Intelligent Agent

被引：1
|
作者：
Ghizlene, Belmekki [1 ]
Zoulikha, Mekkakia [1 ]
Pomares, Hector [2 ]
机构：
[1] USTO MB, Oran, Algeria
[2] Univ Granada, CITIC UGR Res Ctr, Dept Comp Architecture & Technol, Granada, Spain
来源：
ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT II | 2019年 / 11507卷
关键词：
Driver drowsiness detection; Deep neural networks; CNN; Assistant agent; YOLO real-time object detection; Haar cascade;
D O I：
10.1007/978-3-030-20518-8_58
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper we present a new approach to discern and handle driver's drowsiness. This task is usually based only on its detection, without providing any intelligent feedback appropriated to the situation of the driver, and focusing only on the eyes. The response is usually a simple beep alarm which is not enough to wake up or keep the driver awake all along the road. The innovation in our method resides first in the use of a combination of Haar cascades and deep convolutional neural networks for fast detection of the state of the driver and second, the use of an intelligent assistant agent who will follow up the driver by the front camera of his phone, and tries to take care of his security, and the security of the others.
引用
收藏
页码：699 / 708
页数：10
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