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Kesihambigai22 / repository
AI-powered Driver Monitoring and Vehicle Anomaly Detection System using ESP32, OpenCV, MediaPipe Face Landmarker, EAR-based drowsiness detection, and real-time RPM anomaly monitoring.
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Embedded Systems | Computer Vision | Real-Time Vehicle Safety System
This project presents a real-time AI-powered driver safety and vehicle anomaly monitoring system using ESP32, OpenCV, and MediaPipe.
The system continuously monitors:
When abnormal conditions are detected:
The project combines Artificial Intelligence, Embedded Systems, and Real-Time Monitoring into a low-cost intelligent vehicle safety prototype.
Real-time drowsiness detection
RPM anomaly monitoring
ESP32 embedded control system
OpenCV + MediaPipe facial landmark detection
PWM motor speed control
Serial communication between Python and ESP32
Automatic safety response system
Low-cost embedded AI implementation
Webcam
↓
OpenCV Frame Capture
↓
MediaPipe Face Landmark Detection
↓
EAR Calculation
↓
Drowsiness Decision
↓
Serial Communication
↓
ESP32
↓
RPM Monitoring using IR Sensor
↓
Motor PWM + Buzzer Alert
| Component | Quantity |
|---|---|
| ESP32 Dev Board | 1 |
| Webcam | 1 |
| IR Sensor Module | 1 |
| L298N Motor Driver | 1 |
| DC Motor | 1 |
| Active Buzzer | 1 |
| Breadboard |
Selected from shared topics, language and repository description—not editorial ratings.
sabiha-naguru /
AI-powered driver monitoring system using OpenCV, Python, Arduino, and IoT to detect driver drowsiness and alcohol consumption in real time.
49/100 health| 1 |
| Jumper Wires | Multiple |
AI-Based-Driver-Monitoring-and-Vehicle-Anomaly-Detection-System/
│
├── Hardware/
│ ├── README.md
│ └── circuit.png
│
├── doc/
│ ├── MPMC_Report.docx
│ ├── README.md
│ └── block.png
│
├── result/
│ ├── README.md
│ ├── demo.mp4
│ └── setup.png
│
├── software/
│ ├── README.md
│ ├── drowsiness_detection.py
│ └── esp32_driver_monitoring.ino
│
└── README.md
The system uses MediaPipe facial landmark detection to monitor eye movements in real time.
Eye Aspect Ratio (EAR) is calculated continuously.
If EAR remains below threshold for consecutive frames:
An IR sensor monitors motor RPM using pulse interrupts.
ESP32:
If:
Then:
| Test Scenario | Result |
|---|---|
| Eyes Open | Stable operation |
| Eyes Closed | Drowsiness detected |
| Normal RPM | No anomaly |
| RPM Abnormal | Alert activated |
| Combined Conditions | Full safety response |
Demo Video available in:
/result/demo.mp4
Complete project documentation available in:
/doc/MPMC_Report.docx
Documentation includes:
git clone https://github.com/Kesihambigai22/AI-Based-Driver-Monitoring-and-Vehicle-Anomaly-Detection-System.git
pip install opencv-python mediapipe numpy pyserial
python drowsiness_detection.py
Upload esp32_driver_monitoring.ino using Arduino IDE.
Improves road safety through early fatigue detection.
Demonstrates low-cost intelligent transportation systems.
Supports safer and smarter transportation infrastructure.
This project is developed for academic and research purposes.
Embedded Systems ESP32 Computer Vision OpenCV
MediaPipe Driver Monitoring Drowsiness Detection
IoT Vehicle Safety Edge AI