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
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.6
- 발행연도
- 2024.12
- 수록면
- 553 - 561 (9page)
- DOI
- 10.5573/IEIESPC.2024.13.6.553
이용수
초록· 키워드
오류제보하기
Traditional basketball training methods have shortcomings such as insufficient efficiency and low training specificity, and the effectiveness of traditional basketball training strategies is difficult to guarantee. The research on basketball robots can help basketball players practice in daily training, save training costs and improve training efficiency. The field of basketball robot research, it involves many aspects of knowledge learning, such as deep learning, robot kinematics, robot control, etc., which has very high research value and significance. Basketball has the characteristics of fast speed, which brings great difficulty to the research of basketball. According to the real-time and accuracy requirements of basketball robot vision system, aiming at the shortcomings and problems of traditional methods, combined with the current rapid development of neural network method, this paper carries out the research on basketball target detection and rotating ball trajectory prediction based on deep learning. By stacking the neural network, we can achieve the task of predicting the trajectory, and meet the real-time and certain accuracy. The experimental results show that compared with the traditional physical model, the network in this paper has higher anti-interference ability and accuracy. The proposed network has significant realtime advantages in predicting time, and its accuracy in predicting points is closer to the true value. Compared with traditional algorithms, the accuracy has been greatly improved. It can help basketball players practice in daily training, saving training costs and improving training efficiency. In subsequent basketball training, the system in this article can be used for auxiliary training.
목차
Abstract
1. Introduction
2. Related Work
3. Design of Application Model
4. Results and Analysis
5. Conclusion
Reference
참고문헌 (32)
참고문헌 신청
Liu L, Hodgins J. Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning. ACM Transactions on Graphics (TOG), 2018, 37(4): 1-14.
Li B, Xu X. Application of artificial intelligence in basketball sport. Journal of Education, Health and Sport, 2021, 11(7): 54-67.
Liu N, Liu P. Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things. The Journal of Supercomputing, 2022, 78(1): 123-143.
Tsai W L, Pan T Y, Hu M C. Feasibility study on virtual reality based basketball tactic training. IEEE Transactions on Visualization and Computer Graphics, 2020.
Wen P C, Cheng W C, Wang Y S, et al. Court reconstruction for camera calibration in broadcast basketball videos. IEEE transactions on visualization and computer graphics, 2015, 22(5): 1517-1526.
Li B, Xu X. Application of artificial intelligence in basketball sport. Journal of Education, Health and Sport, 2021, 11(7): 54-67.
Liu N, Liu P. Goaling recognition based on intelligent analysis of real-time basketball image of Internet of Things. The Journal of Supercomputing, 2022, 78(1): 123-143.
Tsai W L, Pan T Y, Hu M C. Feasibility study on virtual reality based basketball tactic training. IEEE Transactions on Visualization and Computer Graphics, 2020.
Wen P C, Cheng W C, Wang Y S, et al. Court reconstruction for camera calibration in broadcast basketball videos. IEEE transactions on visualization and computer graphics, 2015, 22(5): 1517-1526.
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