Real-Time Sleepiness Detection for Driver State Monitoring System
To address the risk of traffic accidents caused by driver fatigue, this paper proposes a real-time drowsiness detection method. The approach integrates online dynamic template matching with Kalman filtering to achieve robust eye tracking under varying illumination and head pose disturbances. Eye state (open/closed) classification is performed at millisecond-level latency using Histogram of Oriented Gradients (HOG) features and a Support Vector Machine (SVM) classifier; an alarm is triggered when consecutive eye closure exceeds a predefined threshold. The key innovation lies in a novel tracking framework that synergistically combines adaptive template updating with motion prediction, significantly enhancing stability in complex in-vehicle environments. Experimental results demonstrate an average system response latency of less than 30 ms and an eye-closure classification accuracy of 94.2%, satisfying both real-time processing and reliability requirements for embedded automotive platforms.