Simple Human Fall Surveillance System Based on Person Detection

Human fall is a common problem that often occurs with the elderly, disabled people, and people with bone diseases and neurological diseases. Sometimes, it also comes from human carelessness. Detecting and warning of human falls can minimize the unfortunate risks. Therefore, human fall detection has been widely applied in medical care and surveillance systems. This paper proposes a simple human fall surveillance system based on a person detection network. This system utilizes the pre-trained YOLOv8 network architecture with a related person body dataset. The proposed system reduces the computational complexity and simplifies the use of available datasets for building a surveillance system. As a result, the proposed system achieves the best speed at 206 Frames per second (FPS) when testing on a GeForce GTX 1080Ti 11GB GPU.