지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.1
- 수록면
- 239 - 245 (7page)
- DOI
- 10.5370/KIEE.2022.71.1.239
이용수
초록· 키워드
오류제보하기
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.
목차
Abstract
1. 서론
2. 본론
3. 연구결과
4. 결론
References
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UCI(KEPA) : I410-ECN-0101-2022-560-000128928