Real-time Embedded Demo System for Fall Detection under 15W Power.

Fall is one of the major threats to the safety and life quality of elderly and patients. In this paper, an embedded demo system for real-time spatial-temporal fall detection on demo video is proposed. It is built upon MicroSoft Kinect V2 for 3D visual sensing, which is insensitive to illumination change. Meanwhile, NVIDIA Jetson AGX Xavier under 15W power serves as the embedded computing processor. To fit the proposed demo system, a novel real-time spatial-temporal fall detection approach based on deep learning technology is also proposed, with end-to-end running capacity. To our knowledge, this is the first spatial-temporal fall detection method in end-to-end way. In particular, it formulates spatial-temporal fall detection problem as a object detection like task. It first compresses the depth video clip captured by multi-scale temporal sliding window into a compact dynamic image, with fall's rich motion information. Consequently, fall is detected on dynamic image with YOLOv3-Tiny of high running efficiency and promising effectiveness from spatial perspective. Besides real-time live running capacity of our demo system, the experiments on 1 challenging datasets also verify the superiority of the proposed spatial-temporal fall detection approach.