Abstract
Drowsiness detection is essential for improving safety in areas such as transportation and workplace health. This study presents a real-time system designed to detect drowsiness using the Eye Aspect Ratio (EAR) and facial landmark detection techniques. The system leverages Dlibs pre-trained shape predictor model to accurately detect and monitor 68 facial landmarks, which are used to compute the EAR. By establishing a threshold for the EAR, the system identifies when eyes are closed, indicating potential drowsiness. The process involves capturing a live video stream, detecting faces in each frame, extracting eye landmarks, and calculating the EAR to assess alertness. Our experiments show that the system reliably detects drowsiness with high accuracy while maintaining low computational demands. This study offers a strong solution for real-time drowsiness detection, with promising applications in driver monitoring and workplace safety. Future research will investigate incorporating additional physiological and contextual data to further enhance detection accuracy and reliability.
Abstract (translated)
昏睡检测对于改善交通和工作场所的健康安全至关重要。这项研究介绍了一种实时系统，用于通过Eye Aspect Ratio（EAR）和面部 landmark 检测技术检测昏睡。该系统利用了Dlibs预训练的形状预测模型，准确检测并监控68个面部表情标记物，这些标记物用于计算EAR。通过建立EAR的阈值，系统在眼睛关闭时识别眼睛，表明存在昏睡。该过程涉及捕获实时视频流，检测每帧的面部，提取眼部标记物，并计算EAR以评估警觉性。我们的实验结果表明，该系统在保持低计算负担的同时，以高准确度可靠地检测昏睡。这项研究为实时昏睡检测提供了一个强大的解决方案，在驾驶员监控和职场安全方面具有光明的前景。未来研究将调查引入其他生理学和上下文数据，以进一步增强检测准确性和可靠性。
URL
https://arxiv.org/abs/2408.05836
