Towards a template matching approach for human fall detection
The technology and products related to fall detection has always been in high demand within the security and healthcare industries. A fall detection system must provide a reliable notification mechanism to significantly reduce the risk and medical care costs associated with falls. The rapid developments in Smart Environments and the Internet of Things paradigms, together with the increasing number of low-cost cameras, form a favorable setup for vision-based fall detection systems. In this paper, we propose a novel vision-based fall detection technique that uses fall templates. Fall templates represent human shape variations in case of a fall. We implemented different template matching techniques to evaluate the performance of our proposed template-based approach. Performance evaluation shows encouraging results for single camera-based live deployments.
Contour detection, Edge detection, Fall detection, Foreground detection, Template matching
Chaudhari, Snigdha, and Razib Iqbal. "Towards a Template Matching Approach for Human Fall Detection." In 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pp. 632-637. IEEE, 2021.
Proceedings - 2021 IEEE 45th Annual Computers, Software, and Applications Conference, COMPSAC 2021