Computer Vision · Edge AI · 2026
DriveGuard AI — Driver Monitoring System
Multi-person driver safety monitoring with Z-Mesh dynamic baseline calibration.
up to 6
People tracked
5 sec
Calibration
0–100
Risk index
100%
Runs offline
The problem
Generic drowsiness detectors use fixed thresholds, so they misfire on faces that differ from the average — and cloud processing of in-car video raises real privacy concerns.
The solution
A fully offline system that profiles each driver's face on startup, builds a personal statistical baseline, and only raises an alert when an anomaly persists long enough to be real.
Detection pipeline
- Live frames captured with OpenCV and tracked across frames by Euclidean identity mapping
- MediaPipe Face Mesh landmarks → Eye Aspect Ratio, Mouth Aspect Ratio, 3D head pose (solvePnP)
- YOLOv8 detects cell phones inside each registered bounding box every N frames to save CPU
- Z-score anomaly detection against the driver's own calibration baseline
Temporal confirmation
Raw anomalies must persist before an alarm fires, which removes blinks and mirror checks.
- Drowsiness — eyes closed for 2.0 s
- Yawning — mouth wide open for 1.5 s
- Distraction — head turned away for 2.5 s, with hysteresis under 0.4 s
- Phone usage — phone held for 1.5 s, ignoring occlusions under 0.8 s
Explainable risk engine
- Base weights: phone +30, drowsiness +25, distraction +15, yawning +10
- Escalation while a violation stays unresolved, plus a repetition penalty
- Decay of 10 points/sec once the driver returns to a safe state
- Dark-mode command center with live telemetry, event log and trip summary
Results & impact
- ✓Personal calibration removes the false alerts that fixed thresholds produce
- ✓Runs entirely on edge hardware — no video ever leaves the vehicle
- ✓Session reports explain what happened and which factors drove the risk score
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