实时检测司机眨眼状态，防困预警更及时
Real-Time Sleepiness Detection for Driver State Monitoring System
- 用动态模板+卡尔曼滤波追踪眼部位置
- 眨眼闭合超阈值即触发警报，响应快
- 适合车载安全系统开发与智能驾驶研究
驾驶员面部监控系统可利用计算机视觉技术检测疲劳状态，这是导致诸多事故的重要因素。本文提出一种实时驾驶员眼态检测方法：首先检测人脸并定位眼区，随后采用基于归一化互相关法的在线动态模板匹配结合卡尔曼滤波，实现后续帧中眼部位置的持续跟踪；再使用支持向量机结合方向梯度直方图（HOG）特征对眼睛状态进行分类，判断为睁眼或闭眼。若眼睛持续闭合超过设定时间，则判定司机已入睡，并触发警报。
原文摘要 · Abstract (English)
A driver face monitoring system can detect driver fatigue, which is a significant factor in many accidents, using computer vision techniques. In this paper, we present a real-time technique for driver eye state detection. First, the face is detected, and the eyes are located within the face region for tracking. A normalized cross-correlation-based online dynamic template matching technique, combined with Kalman filter tracking, is proposed to track the detected eye positions in subsequent image frames. A support vector machine with histogram of oriented gradients (HOG) features is used to classify the state of the eyes as open or closed. If the eyes remain closed for a specified period, the driver is considered to be asleep, and an alarm is triggered.
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