signalSafe is a real-time AI-powered security monitoring system that detects potential distress gestures using computer vision and machine learning. The system uses a camera feed, analyzes hand landmarks using MediaPipe, classifies gestures using a trained ML model, and displays alerts in a web-based Security Operations Center (SOC) dashboard. It supports: Real-time distress detection, Live alert, Alert logging & false positive tracking, Escalation to campus safety, ML confidence scoring.

In large campuses and public spaces, silent distress signals can go unnoticed. signalSafe provides: Automated gesture-based distress detection, Real-time monitoring dashboard, Structured incident logging, Reduced response time.

Tech Stack: Backend: Python, Flask, OpenCV, MediaPipe, Scikit-learn, and Joblib Frontend: HTML, CSS, and JavaScript Machine Learning: Custom trained classifier (distress_classifier.joblib), and Training data stored in X_hand.npy and y_hand.npy