[论文解读] Driver Drowsiness Detection System: An Approach By Machine Learning Application

Jagbeer Singh, Ritika Kanojia+3 more|arXiv (Cornell University)|Mar 11, 2023

Sleep and Work-Related Fatigue被引用 14

一句话总结

这篇论文提出一个基于机器学习的系统，通过人脸和眼睛跟踪来检测驾驶员打瞌睡，将眼睛图像与数据集进行匹配，当眼睛闭合时发出警报，准确率约为80%。

ABSTRACT

The majority of human deaths and injuries are caused by traffic accidents. A million people worldwide die each year due to traffic accident injuries, consistent with the World Health Organization. Drivers who do not receive enough sleep, rest, or who feel weary may fall asleep behind the wheel, endangering both themselves and other road users. The research on road accidents specified that major road accidents occur due to drowsiness while driving. These days, it is observed that tired driving is the main reason to occur drowsiness. Now, drowsiness becomes the main principle for to increase in the number of road accidents. This becomes a major issue in a world which is very important to resolve as soon as possible. The predominant goal of all devices is to improve the performance to detect drowsiness in real time. Many devices were developed to detect drowsiness, which depend on different artificial intelligence algorithms. So, our research is also related to driver drowsiness detection which can identify the drowsiness of a driver by identifying the face and then followed by eye tracking. The extracted eye image is matched with the dataset by the system. With the help of the dataset, the system detected that if eyes were close for a certain range, it could ring an alarm to alert the driver and if the eyes were open after the alert, then it could continue tracking. If the eyes were open then the score that we set decreased and if the eyes were closed then the score increased. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80% and helps to reduce road accidents.

研究动机与目标

• 解决因疲劳驾驶导致的高发交通事故。
• 开发一种利用面部和眼睛跟踪线索的实时打盹检测方法。
• 评估在眼睛持续闭合达到阈值时发出警报的基于 ML 的系统。
• 证明眼睛状态（睁眼/闭眼）与疲劳之间的相关性，以降低事故发生。

提出的方法

• 实时识别驾驶员的面部并跟踪眼睛。
• 提取眼睛图像并将其与数据集进行比较以分类疲劳。
• 使用基于闭眼的评分机制，其中闭眼会增加分数，睁眼会降低分数。
• 如果眼睛在设定范围内持续闭合则触发警报。
• 目标达到近似 80% 的实时性能与准确率。

实验结果

研究问题

• RQ1 实时眼睛跟踪和面部分析是否能可靠地检测驾驶员的疲劳？
• RQ2 基于数据集的眼睛图像匹配在疲劳分类中的效果如何？
• RQ3 闭眼分数是否与疲劳相关，以触发及时警报？

主要发现

• 系统在检测疲劳方面的准确率约为 80%。
• 当眼睛闭合达到指定持续时间时触发警报。
• 眼睛状态跟踪（睁眼/闭眼）为动态评分机制提供信息。
• 实时处理是重点，以便及时通知驾驶员。
• 该方法强调通过早期疲劳检测来减少交通事故。