RETRACTED ARTICLE: Application of Deep Learning Method in Automatic Collection and Processing of Video Surveillance Data for Basketball Sports Prediction

Jiang, Han; Qiu, Ting; Thilak, K. Deepa

doi:10.1007/s13369-021-05884-1

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…This system may help identify players' positions and roles in basketball games. Jiang, et al (2021) [ 10 ] proposed a video framework by deep learning to build a player position system. They used traditional regression techniques to determine each person's position so that the player moves toward the ball position.…”

Section: Introductionmentioning

confidence: 99%

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

Wang

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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This study explores the application of deep reinforcement learning (DRL) in the Internet of Things (IoT) sports game design. The fundamentals of DRL are deeply understood by investigating the current state of IoT fitness applications and the most popular sports game design architectures. The research object is the ball return decision problem of the popular game of table tennis robot return. Deep deterministic policy gradients are proposed by applying DRL to the ball return decision of a table tennis robot. It mainly uses the probability distribution function to represent the optimal decision solution in the Markov Model decision process to optimize the ball return accuracy and network running time. The results show that in the central area of the table, the accuracy of returning the ball is higher, reaching 67.2654%. Different tolerance radii have different convergence curves. When r = 5 cm, the curve converges earlier. After 500,000 iterations, the curve converges, and the accuracy rate is close to 100%. When r = 2 cm and the number of iterations is 800,000, the curve begins to converge, and the accuracy rate reaches 96.9587%. When r = 1 cm, it starts to converge after 800,000 iterations, and the accuracy is close to 56.6953%. The proposed table tennis robot returns the ball in line with the requirements of the actual environment. It has practical application and reference value for developing IoT fitness and sports.

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Section: Introductionmentioning

confidence: 99%

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

Wang

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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“…In addition, the position of the player was determined by traditional regression techniques and their movement towards the point of landing. Simulation analysis of the feasibility, performance, and system efficiency demonstrated the reliability of the framework [10]. Özkan studied the electrophysiological basis of predicting free throw hits in wheelchair basketball players.…”

Section: Related Workmentioning

confidence: 99%

“…The model increases [10] 89 92 [9] multi-scale position enhancement network, these studies have demonstrated efforts to improve estimation accuracy and computational efficiency. In addition, Jiang and Özkan [10,11] showed the practical application value of the technique by applying the human pose estimation technique to specific scenarios, such as basketball motion monitoring and wheelchair basketball player behavior prediction. These studies have improved the accuracy and efficiency of pose estimation and expanded its application in sports, medicine, and other fields.…”

Section: Improve the Applicability Of The Modelmentioning

confidence: 99%

“…In equation (10), i m represents the number of feature points of the i joint point in the g frame, and i n represents the number of feature points of the i joint point in the h frame. When the similarity is higher than the threshold set by the threshold, the joint points in the previous sequence are used as candidates, and if the similarity is lower than the set threshold, the joint point data of this frame is cleared.…”

Section: ()mentioning

confidence: 99%

“…represent the extracted bounding box of the body part. The feature points extracted similarity between the previous frame pose of the body and the current pose is expressed by equation(10).…”

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confidence: 99%

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Basketball Fixed-point Shot Hit Prediction Based on Human Pose Estimation Algorithm

Li,

Hua

2024

IJCAI

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As computer vision and artificial intelligence develop, the research on basketball fixed-point shooting hit prediction based on human pose estimation algorithm becomes a topic of great concern. To construct a basketball fixed-point shooting hit prediction model, a new object detection algorithm was designed, and the You Only Look Once version 5 algorithm was optimized on the basis of GIoU loss function and Convolutional Block Attention Module. Then, a new human pose estimation algorithmwas designed based on the OpenPose algorithm. Results showed that the average accuracy of the improved YOLOv5 algorithm reached 95.34% when the number of iterations was 50. In the comparison among improved OpenPose and other algorithms, improved OpenPose performed better, with a recall rate of 96.23%, an accuracy of 87.16%, a precision of 89.75%, and an F1 value of 88.19%. In the comparison with other models, the area of receiver operating characteristic curve was the largest, reaching 0.974, and the F1 value, accuracy and recall rate of the research model were the highest, reaching 95.54%, 96.39% and 98.25%, respectively. Results show that it effectively predicts the shoot hit of basketball fixed-point shooting, which provides a useful reference for tactical analysis and player performance evaluation in basketball games.Povzetek: Raziskava predstavlja napovedni model za zadetke pri košarkarskem metu s fiksne točke na osnovi algoritma za ocenjevanje človeške drže, kar je koristno za taktično analizo in ocenjevanje igralcev v košarki.

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An assisted teaching algorithm for basketball shooting based on object decomposition

Liu

2022

Soft Comput

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RETRACTED ARTICLE: Application of Deep Learning Method in Automatic Collection and Processing of Video Surveillance Data for Basketball Sports Prediction

Cited by 8 publications

References 35 publications

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

The Design of Sports Games under the Internet of Things Fitness by Deep Reinforcement Learning

Basketball Fixed-point Shot Hit Prediction Based on Human Pose Estimation Algorithm

An assisted teaching algorithm for basketball shooting based on object decomposition

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