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학술대회 Human-skeleton based Fall-Detection Method using LSTM for Manufacturing Industries
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저자
정성일, 강성주, 전인걸
발행일
201906
출처
International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2019, pp.1-4
DOI
https://dx.doi.org/10.1109/ITC-CSCC.2019.8793342
협약과제
19MS1200, SW통합 개발자 환경(SDK) 및 공통 라이브러리 개발, 나갑주
초록
According to the statistics of the Korea Occupational Safety Health Agency, the incidences of falls in the manufacturing industry are increasing. In this paper, we introduce a fall-detection method based on skeleton data obtained from a 2D RGB CCTV Camera installed on the manufacturing floor. We proposed feature-extraction methods to improve of fall-detection accuracy and the construction of a fall-detection system using LSTM. Experiments were conducted through public datasets (URFD and SDUFall) to find feature-extraction methods that can achieve high classification accuracy. The experimental results showed that the proposed method is more effective in detecting falls than raw skeleton data which are not processed anything.
키워드
Fall-detection, LSTM, Machine learning, Smart Factory
KSP 제안 키워드
CCTV Camera, Detection Method, Detection accuracy, Extraction method, Fall Detection, Feature extractioN, Intrusion detection system(IDS), Occupational safety, Public Datasets, Skeleton data, Smart Factory