RESEARCH AND APPLICATION OF COMPUTER VISION IN DRIVER MONITORING SYSTEMS ON MOBILE DEVICE
Keywords
DOI:
https://doi.org/10.34238/tnu-jst.15917Abstract
This study proposes a driver-state monitoring system for Android smartphones that uses Google MediaPipe to extract 468 facial landmarks from camera frames. The system detects eye closure, yawning, and head-orientation deviation using three geometric features: the three-dimensional eye aspect ratio, the three-dimensional mouth aspect ratio, and the relative depth-coordinate difference between selected facial landmarks. The processing pipeline avoids additional sensors and relies on normalized landmark geometry instead of calibrated camera parameters. A time-based filtering mechanism triggers alerts only when the detected state persists for a predefined duration, thereby reducing noisy warnings caused by short transient movements. Experiments with 12 participants under bright and low-light conditions showed precision values of 91.47% for eye closure, 88.15% for head deviation, and 84.76% for yawning; recall values of 89.35%, 86.11%, and 82.41%, respectively; an average harmonic mean of Precision and Recall of 87.03%; and detection latency of 287-421 milliseconds. The results indicate the initial feasibility of smartphone-based deployment, although performance remains affected by lighting and fixed thresholds.Downloads
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