CampusFall: A Multi-Perspective Indoor and Outdoor Fall Detection Dataset Based on Campus Surveillance

Falls, a common type of accident, especially among the elderly and those with mobility impairments, potentially leading to serious physical injuries and health issues. Fall detection refers to the use of sensors, monitoring equipment, or other technological means to monitor and identify occurrences of falls. Currently, a lot of research on fall detection from various aspects such as vision, wearable devices, and multi-modal. However, current vision-based fall detection datasets are limited to single indoor scenarios and do not consider outdoor scenarios. Therefore, in this paper we propose a multi-perspective indoor and outdoor scenarios fall detection dataset based on campus surveillance, which include both indoor and outdoor campus scenarios. We employed YOLOv5 to carry out experiments on our proposed dataset as a benchmark. Moreover, we undertook comparative experiments against other datasets and assessed the richness of our dataset. The experiment results reveal that our dataset encompasses more diverse scenarios than other vision-based fall detection dataset.