Shooting Prediction Based on Vision Sensors and Trajectory Learning
Source abstract
Abstract
Basketball has become one of the most popular sports and is generally popular in international sports events. However, how to effectively achieve shooting prediction and then guide shooting has become a major challenge. Different from the classical manual observation method, this paper proposes a real-time shooting prediction method based on vision sensors and trajectory learning. In the research, we first extracted the basketball trajectory information on template matching and centroid calculation and then obtained a smooth trajectory curve through interpolation. Taking the change of x, y coordinate position, height Y, and distance D from the shooting point during the instantaneous movement as basic features, four machine learning algorithms were used to analyze the impact of different feature combinations on the shooting prediction. Finally, we analyzed the minimum trajectory point requirements predicted when making a shot. The experimental results show that our method can effectively predict the effect of shooting when the feature combination is basketball height and time. When the interpolation density is high (the total number of trajectory points is 116), the overall accuracy can reach more than 90%, and only one-third of the effective trajectory length is required, which effectively helps athletes improve their shooting percentage and assist referees in daily training.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sports Performance and Training · Sports Analytics and Performance · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Sensor and Big Data Laboratory, Northeastern University, Qinhuangdao 066000, China
Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao 066000, China
Zhejiang Institute of Communication, Hangzhou 311112, China
State Key Laboratory of Dynamic Measurement Technology, School of Instrumentation and Electronics, North University of China, Taiyuan 038507, China
Shenzhen Research Institute, City University of Hong Kong, Shenzhen 518057, China
Funding
National Natural Science Foundation of China · Hebei Natural Science Foundation · Fundamental Research Funds for the Central Universities · Administration of Central Funds Guiding the Local Science and Technology Development · Shenzhen Science and Technology Innovation Commission (SZSTI)
References (31)
Show source-provided references
1. Wearable Sport Activity Classification Based on Deep Convolutional Neural Network10.1109/ACCESS.2019.2955545 ↗
2. SAETA: A Smart Coaching Assistant for Professional Volleyball Training10.1109/TSMC.2015.2391258 ↗
3. Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf10.3233/AIS-2009-0021 ↗
4. Dijkhuis, T.B., Blaauw, F.J., Van Ittersum, M.W., Velthuijsen, H., and Aiello, M. (2018). Personalized Physical Activity Coaching: A Machine Learning Approach. Sensors, 18.10.3390/s18020623 ↗
5. Quantitative Evaluation of Gymnastics Based on Multiple MEMS Sensors10.1109/JSEN.2021.3114758 ↗
6. Accurate Recognition of Player Identity and Stroke Performance in Table Tennis Using a Smart Wristband10.1109/JSEN.2021.3060914 ↗
7. Enhancing performance expectancies through positive comparative feedback facilitates the learning of basketball free throw in children10.1016/j.psychsport.2018.03.001 ↗
8. Yin, F., Hu, G., and Shen, W. (2022). Analysis of professional basketball field goal attempts via a Bayesian matrix clustering approach. J. Comput. Graph. Stat., 1–23.10.1080/10618600.2022.2085727 ↗
9. Nguyen, L., Nguyen, N., Rodríguez-Martín, D., Català, A., Pérez-López, C., Samà, A., and Cavallaro, A. (2015, January 7). Basketball Activity Recognition Using Wearable Inertial Measurement Units. Proceedings of the XVI International Conference on Human Computer Interaction, Vilanova, Spain.10.1145/2829875.2829930 ↗
10. Ma, R., Yan, D., Peng, H., Yang, T., Sha, X., Zhao, Y., and Liu, L. (2018, January 5–7). Basketball Movements Recognition Using a Wrist Wearable Inertial Measurement Unit. Proceedings of the 2018 IEEE 1st International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS), Shenzhen, China.10.1109/NSENS.2018.8713634 ↗
11. Kuhlman, N., and Min, C.-H. (2021, January 27–30). Analysis and Classification of Basketball Shooting Form Using Wearable Sensor Systems. Proceedings of the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.10.1109/CCWC51732.2021.9375925 ↗
12. ANN-Enhanced IoT Wristband for Recognition of Player Identity and Shot Types Based on Basketball Shooting Motion Analysis10.1109/JSEN.2021.3128046 ↗
13. Shankar, S., Suresh, R.P., Talasila, V., and Sridhar, V. (2018, January 14–15). Performance measurement and analysis of shooting form of basketball players using a wearable IoT system. 2018 IEEE 8th International Advance Computing Conference (IACC), Greater Noida, India .10.1109/IADCC.2018.8692124 ↗
14. Research on Basketball Shooting Action Based on Image Feature Extraction and Machine Learning10.1109/ACCESS.2020.3012456 ↗
15. Anthropometric assessment of youth national championship basketball players in relation to playing position10.1136/bjsm.2010.078725.119 ↗
16. Wang, Z., Wang, P., Jiang, L., Tang, B., Zhang, C., and Hu, Z. (2017, January 11–12). Analysis of Influencing Factors of Shooting Rate Based on Trajectory Prediction of the Basketball. Proceedings of the 14th Web Information Systems and Applications Conference, WISA 2017, Liuzhou, China.
17. Cheng, C.-Y., Chen, Y.-J., and Lin, S.-Y. (August, January 20). Design and implementation of a vision-based basketball shooting robot. Proceedings of the 2005 IEEE International Conference on Mechatronics, Niagara Falls, ON, Canada.
18. Oughali, M.S., Bahloul, M., and El Rahman, S.A. (2019, January 3–4). Analysis of NBA Players and Shot Prediction Using Random Forest and XGBoost Models. Proceedings of the 2019 International Conference on Computer and Information Sciences (ICCIS), Sakaka, Saudi Arabia.10.1109/ICCISci.2019.8716412 ↗
19. Image Matching Pursuit Algorithm Based on SSDA
20. Simultaneous Detection of Defects in Electrical Connectors Based on Improved Convolutional Neural Network
21. Analysis of Image Processing Using Morphological Erosion and Dilation10.1088/1742-6596/2071/1/012033 ↗
22. Rahmani, H., Haddadi, M., Boulouma, S., Mellit, A., Cheikh, R., and Belmili, H. (2022). A new digital phase estimation technique based on centroid formula. Electr. Power Syst. Res., 212.10.1016/j.epsr.2022.108631 ↗
23. Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation10.1016/j.patcog.2017.09.038 ↗
24. Comparative study on classifying human activities with miniature inertial and magnetic sensors10.1016/j.patcog.2010.04.019 ↗
25. Efficient kNN Classification With Different Numbers of Nearest Neighbors10.1109/TNNLS.2017.2673241 ↗
26. Zhao, Y., Li, J., Wang, X., Liu, F., Shan, P., Li, L., and Fu, Q. (2022). A Lightweight Pose Sensing Scheme for Contactless Abnormal Gait Behavior Measurement. Sensors, 22.10.3390/s22114070 ↗
27. Deep Learning Based Analysis of Breast Cancer Using Advanced Ensemble Classifier and Linear Discriminant Analysis10.1109/ACCESS.2020.3005228 ↗
28. Hb vsEMG signal classification with time domain and Frequency domain features using LDA and ANN classifier10.1016/j.matpr.2020.09.091 ↗
29. Decision Trees: A Recent Overview10.1007/s10462-011-9272-4 ↗
30. Jothi, K.A., Subburam, S., Umadevi, V., and Hemavathy, K. (2021). Heart disease prediction system using machine learning. Mater. Today Proc., in press.
31. Machine Learning-Based Fast Intra Mode Decision for HEVC Screen Content Coding via Decision Trees10.1109/TCSVT.2019.2903547 ↗