A Security-Centric Deep Learning Enabled Camera Solution for Real-Time Human Fall Detection
Hamid Reza Tohidypour, Mahsa T. Pourazad, Panos Nasiopoulos · 2022
Automatic human real-time fall detection is a challenging task in remote healthcare, demanding a non-intrusive, secure and affordable solution. In this paper, we present a real-time hardware system that uses a deep learning model for fall detection embedded in a color camera. To reduce the startup delay and achieve real-time performance for the inference phase, we optimized our model using TensorRT. In addition, we addressed the board memory limitation using virtual memory and linear memory allocation and garbage collection. Moreover, GStreamer was used to perform most of the video processing using Jetson's GPU. Our live evaluation shows that our system achieved the accuracy of 84.44% and real-time performance.