Computer Vision Safety System
Suraksha Dristi
Problem: In classroom and public spaces, silent distress signals often go unnoticed and emergency response can be delayed.
System Architecture
- Video ingestion from webcam and IP camera streams.
- Gesture and face detection with MediaPipe and OpenCV.
- Distress trigger pipeline to alert administrators in real time.
Tech Stack: Python, OpenCV, MediaPipe, Haar Cascade, NumPy
Results: 85% gesture recognition accuracy at ~18 FPS and 40% faster simulated emergency response in controlled tests.