인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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
- 저자정보
피인용 0
초록·키워드
In this study, a key gesture recognition method of basketball based on improved YOLOv3 algorithm is proposed. By optimizing the algorithm, by improving the feature extraction network and introducing spatiotemporal information, we construct a data set containing a large number of basketball actions for experimental verification. The enhanced algorithm demonstrated a 92% success rate in recognizing crucial poses, outperforming the original YOLOv3 algorithm by 10%, and shows higher efficiency in real-time video processing, and the average processing time per frame is shortened by 20%. The improved YOLOv3 algorithm shows high performance in basketball’s key gesture recognition tasks, and provides a new technical means for scientific training and competition analysis of basketball. The algorithm combines the spatial domain of convolutional networks to enhance the sensory field of the model and extracts the data of human feature points from two dimensions to improve the deficiency of the model fitting ability. Meanwhile, a multi-head attention mechanism is used to enhance the model adaptive capability. In the experimental tests, the proposed model improvement term has a certain performance improvement compared with the traditional algorithm, and the error index of the model is reduced by 1.15 mm and the accuracy is improved by 2.3% compared with the optimal algorithm, which proves the effectiveness and superiority of the proposed method.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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UCI(KEPA) : I410-151-26-02-096409222
