Silent Alert: AI-Enhanced Hand Sign Recognition for Real Time Emergency via CCTV

The proposed system employs AI technologies to analyze hand gestures in real-time as it functions for emergency alert transmission through CCTV camera feeds but works independently from verbal or physical signals. The system operates with Python-based technologies and OpenCV for live video streaming alongside MediaPipe for hand landmark detection through a standard webcam. The system detects predefined gestures including emergency signals through closed fists combined with stop gestures using open palms and victory signs by checking the relative landmark positions on hands. In case the system detects an emergency gesture it retrieves positioning data through IPbased geolocation before automatically sending a message with gesture details and timestamp and coordinates. The system distributes the alert distribution task to different threads to maintain live video streaming processing continuity. The system contains a cooldown function that blocks subsequent alerts when they occur within short time periods. Low-end hardware and accessibility through lack of sensor dependency enable the system to deliver efficient operations. The system provides discreet signals for emergency help requests which prove useful to endangered individuals and people with disabilities and medical patients in crisis situations. Future improvements will create facial authentication capability in addition to SMS monitoring and mobile software release which will turn this product into a life-saving solution for public protection agencies.