An assisted teaching algorithm for basketball shooting based on object decomposition

Liu, Xixiao; Xi, Xuyun

doi:10.1007/s00500-022-07086-9

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…However, the efficacy of these deep learning algorithms hinges on access to large annotated datasets for training. Collaborative efforts between researchers, coaches, and sports organizations are essential to compile and label the requisite data [6]. Additionally, ongoing refinement of algorithms is crucial to adapt to the dynamic nature of basketball gameplay and the diverse styles of players.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Recognition and Evaluation of Technical Movements of Basketball Players Using Deep Learning Algorithms

Sun

2024

jes

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Deep learning algorithms are revolutionizing the analysis of technical movements in basketball players by extracting intricate patterns and insights from vast amounts of video data. By training neural networks on annotated video sequences of basketball games, these algorithms can automatically detect and classify various technical movements such as dribbling, shooting, passing, and defensive maneuvers. The use of deep learning enables the identification of subtle nuances in player movements, facilitating more accurate performance assessment and actionable feedback for athletes and coaches. This paper introduces a novel approach for analyzing basketball player movements utilizing Anthropometrical Variable Assessment Deep Learning (AVADL). By integrating anthropometric variables with deep learning algorithms, AVADL offers a comprehensive framework for accurately recognizing and evaluating technical movements on the basketball court. We present experimental results demonstrating the effectiveness of AVADL across various dataset sizes and player profiles, showcasing high accuracy and performance metrics. The incorporation of anthropometric measurements provides valuable context into the diverse physical attributes of basketball players, enhancing our understanding of their playing style and performance. Experimental results demonstrate the effectiveness of AVADL across various dataset sizes and player profiles, with training accuracies ranging from 90% to 97% and testing accuracies from 85% to 92%. Precision, recall, and F1-Score metrics consistently show values above 0.80, indicating the robustness of the approach. The incorporation of anthropometric measurements provides valuable context into the diverse physical attributes of basketball players, enhancing our understanding of their playing style and performance.

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Section: Introductionmentioning

confidence: 99%