Article
Intelligent Video-Based Fall Surveillance System for Elderly Safety
Elderly people and those who need constant attention are more vulnerable to falls. A real-time vision-based fall detection system utilizing deep learning and human position estimate methods is presented in this study. The suggested method combines MediaPipe Pose for skeletal landmark extraction from video frames with the YOLOv8 model for precise human detection. Using a rule-based decision procedure with time-based validation to minimize false alarms, fall events are detected by examining posture changes and body orientation. The device immediately produces audible and visual alarms when a fall is confirmed. A nonintrusive and effective solution for healthcare monitoring, the system is developed as a Flask-based web application that supports both live video streaming and recorded video analysis.
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