Design of Fatigue Driving Behavior Detection Based on Circle Hough Transform

被引:1
|
作者
Huang, An Chi [1 ]
Yuan, Chun [2 ,6 ]
Meng, Sheng Hui [3 ,4 ,7 ]
Huang, Tian Jiun [5 ]
机构
[1] Tsinghua Univ, Dept Comp Sci, Beijing, Peoples R China
[2] Tsinghua Shenzhen Int Grad Sch, Peng Cheng Lab, Shenzhen, Peoples R China
[3] Putian Univ, New Engn Ind Coll, Putian, Peoples R China
[4] Yango Univ, Sch Artificial Intelligence Coll, Fuzhou, Peoples R China
[5] Natl Kaohsiung Univ, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan
[6] Tsinghua Shenzhen Int Grad Sch, Peng Cheng Lab, Shenzhen 518055, Peoples R China
[7] Yango Univ, Sch Artificial Intelligence Coll, Fuzhou 350015, Peoples R China
基金
国家重点研发计划;
关键词
driver behavior correlation analysis; circle Hough transform; canny edge detector; fatigue evaluation; head gestures; SEGMENTATION; SYSTEM; ALGORITHM; TECHNOLOGIES; VEHICLES; REGIONS; IMAGE;
D O I
10.1089/big.2021.0166
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Chronic fatigue symptoms of jobs are risk factors that may cause errors and lead to occupational accidents. For instance, occupational injuries and traffic accidents stem from overlooking long-term fatigue. According to statistics for fatigue driving, it was found that fatigue driving is one of the main causes of traffic accidents. The resulting decrease in the quality of traffic, as well as impaired traffic flow efficiency and functioning, contributes markedly to the societal costs of fatigue. This article proposes a noninvasive physical method for fatigue detection using a machine vision image algorithm. The main technology was implemented using a software framework based on optimized skin color segmentation and edge detection, as well as eye contour extraction. By integrating machine vision and an optimized Hove transform algorithm, our method mainly identifies fatigue based on the detected target's face, head gestures, mouth aspect ratio (MAR), and eye condition, and then triggers an alarm through an intelligent auxiliary device. Our evaluation results of facial image data analysis showed that with an ideal eye threshold of 0.3, PERCLOS-80 standard, MAR, and head gesture-nod frequency, the method can be used to detect fatigue data accurately and systematically, thereby fulfilling the purpose of alerting a group of high-risk drivers and preventing them from engaging in high-risk activities in an involuntary state.
引用
收藏
页码:1 / 17
页数:17
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