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학술대회 Efficient Human Action Recognition with Dual-Action Neural Networks for Virtual Sports Training
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저자
김종성
발행일
202210
출처
International Conference on Consumer Electronics (ICCE) 2022 : Asia, pp.609-611
DOI
https://dx.doi.org/10.1109/ICCE-Asia57006.2022.9954758
협약과제
22IH1500, 간접 센싱 기반 실시간 연동 AR 실내 스포츠 플랫폼 개발, 김종성
초록
In this paper, we propose an efficient human action recognition method for virtual sports training. In the proposed method, 3D skeleton data of human actions for four kinds of sports, which are composed of ready actions and main ones, are accurately captured by using RGB-D cameras. Then, skeleton features, which are sophisticatedly designed for each sport training, are extracted from captured 3D skeleton data. Finally, a dual-action neural network, which is developed to simultaneously recognize ready actions and main ones for sports training, is trained with extracted skeleton features. The effectiveness of the proposed method based on the dual-action neural network is verified with several real experiments for various sports.
KSP 제안 키워드
3d Skeleton, RGB-D camera, Recognition method, Skeleton data, Sport Training, Virtual sports, human action recognition, neural network, real experiments, skeleton features, sports training