| Title |
Swoon Monitoring System based on YOLOv4-CSP Object Detection Algorithm |
| Authors |
채정우(Jung-woo Chae) ; 김태경(Tae-kyeong Kim) ; 조현종(Hyun-chong Cho) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.1.239 |
| Keywords |
CCTV; Cross Stage Partial Connections; Deep Learning; Object Detection; Swoon |
| Abstract |
When swoon occurs, it is important to detect it immediately. If immediate and quick detection is not possible, additional serious accidents may occur. Therefore, this study proposed swoon monitoring system that can detect swoon from real-time images of CCTV based on the increasing number and installation of CCTV. For the detection of swoon, deep learning based object detection algorithm applied with the Cross Stage Partial connections network model was used. Through this, swoon detection is performed within a single frame, and the amount of computation required for swoon detection is reduced and can have a fast processing speed.
In addition, it is possible to have high detection performance through various BoF and BoS techniques with CSP network model.
The result of swoon detection and classification performance through object detection algorithm showed 94.2% F1-Score, 92.6% mAP and 95.4% Accuracy |