Analysis of Basketball Technical Movements Based on Human-Computer Interaction with Deep Learning

Meng, Xu-Hong; Shi, Hong-Ying; Shang, Weihong

doi:10.1155/2022/4247082

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…This article is about filming the game with a high-resolution camera, and through this filming, some features can be taken, such as the two best players who make three-point or two-point throws, as well as plans and ways to move the players. Then, this data is entered into deep networks to be used in the analysis process [17] Researchers presented a method for analyzing basketball videos to reduce injury, improve the players' skills, as well as help the coach to choose the best players who can decide the outcome in favor of the team [18] A group of researchers suggested a way to identify basketball players through one side camera. It is clear that the side image does not give enough information about the body because a lot of information can be provided in the player's identification coin.…”

Section: Basketballmentioning

confidence: 99%

Computer vision systems and deep learning for the recognition of athlete's movement: A review article

Alsaif,

Abdullah

2023

Tikrit J. Pure Sci.

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The process of detecting people in videos and then tracking their movement is one of the very important topics. The process of tracking people and studying their behaviour could result in a large set of information that can help researchers in studying reactions. The techniques of detection and tracking the movement of people are used in the sports field, where the athlete's movement is studied and analyzed within the game. Based on the information obtained from the process of tracking the athlete's movement, it is possible to improve the playing performance as well as avoid injuries and choose the best playing strategy. In some games, the accuracy of athlete's performance is a measure of the points given to the athlete's like gymnastics. This study reviews a set of articles that relied on computer vision as well as deep learning in the process of distinguishing and analysing the athlete's movement. The articles are confined to the years from 2015 to 2022, dealing with different indoor and outdoor sports. Certainly, the study of indoor games is better because the influence of weather conditions is less than that of outdoor ones. Reviewing the articles demonstrates that relying on computer vision systems is more effective than relying on human rulers, as humans are more prone to error. As for relying on the deep learning techniques for detecting the object, the results are very positive, due to the correct detection of the object. The results of the analysis of this object will be more accurate.

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Section: Basketballmentioning

confidence: 99%

Computer vision systems and deep learning for the recognition of athlete's movement: A review article

Alsaif,

Abdullah

2023

Tikrit J. Pure Sci.

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“…On the other hand, the ability to analyze the actions which occur in a video is essential for automatic understanding of sports. The action recognition techniques can efficiently collect and classify the actions/events in sports video, and consequently help a lot with the sports statistics analysis which is the basis to understand the sports [181], [307]- [311].…”

Section: Applicationsmentioning

confidence: 99%

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

Wu¹,

Wang²,

Bian³

et al. 2022

Preprint

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To understand human behaviors, action recognition based on videos is a common approach. Compared with imagebased action recognition, videos provide much more information. Reducing the ambiguity of actions and in the last decade, many works focused on datasets, novel models and learning approaches have improved video action recognition to a higher level. However, there are challenges and unsolved problems, in particular in sports analytics where data collection and labeling are more sophisticated, requiring sport professionals to annotate data. In addition, the actions could be extremely fast and it becomes difficult to recognize them. Moreover, in team sports like football and basketball, one action could involve multiple players, and to correctly recognize them, we need to analyse all players, which is relatively complicated. In this paper, we present a survey on video action recognition for sports analytics. We introduce more than ten types of sports, including team sports, such as football, basketball, volleyball, hockey and individual sports, such as figure skating, gymnastics, table tennis, tennis, diving and badminton. Then we compare numerous existing frameworks for sports analysis to present status quo of video action recognition in both team sports and individual sports. Finally, we discuss the challenges and unsolved problems in this area and to facilitate sports analytics, we develop a toolbox using PaddlePaddle 1 , which supports football, basketball, table tennis and figure skating action recognition.

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“…By employing convolutional neural networks (CNNs), these algorithms can effectively parse video footage of basketball games, enabling precise detection and tracking of players' movements [2]. This facilitates the automatic identification of specific technical actions such as shooting, dribbling, passing, and defensive maneuvers with remarkable accuracy [3]. Furthermore, recurrent neural networks (RNNs) can be utilized to analyze the temporal sequences of these movements, providing insights into the fluidity, coordination, and efficiency of players' actions over time [4].…”

Section: Introductionmentioning

confidence: 99%

“…One way deep learning algorithms can enhance recognition is through the analysis of video footage [8]. By feeding video clips of basketball games into convolutional neural networks (CNNs), these algorithms can learn to detect and track players' movements with high precision.…”

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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Analysis of Basketball Technical Movements Based on Human-Computer Interaction with Deep Learning

Cited by 8 publications

References 28 publications

Computer vision systems and deep learning for the recognition of athlete's movement: A review article

Computer vision systems and deep learning for the recognition of athlete's movement: A review article

A Survey on Video Action Recognition in Sports: Datasets, Methods and Applications

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

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