Our project develops a real-time video-based system for detecting falls among elderly and at-risk populations. Using computer vision techniques, the system analyzes live camera feeds to identify fall events and distinguish them from normal daily activities. When a fall is detected, the system can trigger alerts to enable a fast response and improve personal safety.