Analyzing Basketball Movements and Pass Relationships Using Realtime Object Tracking Techniques Based on Deep Learning

Yoon, Young; Hwang, Heesu; Choi, Yongjun; Joo, Minbeom; Oh, Hyeyoon; Park, Insun; Lee, Keon-Hee; Hwang, Jin‐Ha

doi:10.1109/access.2019.2913953

Cited by 57 publications

(30 citation statements)

References 32 publications

(33 reference statements)

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“…Then, the convolution neural network is used to extract the spatial features of GCMPs, and the layup and 2-point ball are classified in detail. Finally, the basketball events are divided into three points success/failure, free throw success/failure, other two points success/failure, layup success/failure, and slam success/failure [ 23 ]. It can be expressed as V 5−event ={3 − point, free throw, layup, other 2 − point, dunk}.…”

Section: The Design Of the Teaching System Of Basketball Tactical Basismentioning

confidence: 99%

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Hua

2021

Computational Intelligence and Neuroscience

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The traditional basketball teaching mode cannot meet the needs of students for the basic cooperation of basketball tactics. Therefore, a basic cooperation teaching system of basketball tactics based on artificial neural network is studied and designed. The system has a professional basketball game video tactical learning module. The events in the basketball game video are classified through a convolutional neural network and combined with the explanation of teachers to make the students have an intuitive understanding of the basic cooperation of basketball tactics and then design the basketball game module based on a BP neural network to provide students with an online basketball tactics training platform. Finally, the teacher scores the performance of the actual on-site training students in the basic cooperation of basketball tactics through the tactical scoring module on the system. The results show that after the introduction of global and collective motion patterns, the classification accuracy of the convolutional neural network is improved by 22.48%, which has significant optimization. The average accuracy of basketball game video event classification is 62.35%, and the accuracy of snatch event classification is improved to 95.28%. The recognition rate of the BP neural network combined with momentum gradient descent method is 75%, the number of weight adjustment is less, and the memory is small while ensuring fast running speed. Students who accept the basic basketball tactics cooperation teaching system based on the artificial neural network for basketball teaching have an overall score of 27.99 ± 2.11 points The overall score of exchange defense cooperation was 24.12 ± 2.03, which was higher than that of the control group. The above results show that the basketball tactical basic cooperation teaching system based on the artificial neural network has a good teaching effect in improving students’ basketball tactical basic cooperation ability.

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Section: The Design Of the Teaching System Of Basketball Tactical Basismentioning

confidence: 99%

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Hua

2021

Computational Intelligence and Neuroscience

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“…During continuous movement, the athletes' upper and lower limbs keep a continuous periodic transformation. Therefore, unit actions can be divided according to specific action data (Chau et al, 2019 ; Yoon et al, 2019 ; Stübinger et al, 2020 ). When the body movement is described, angular velocity can be taken as the reference base of data division due to its intuitive advantage of data.…”

Section: Methodsmentioning

confidence: 99%

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Tang

2021

Front. Neurorobot.

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In order to effectively prevent sports injuries caused by collisions in basketball training, realize efficient shooting, and reduce collisions, the machine learning algorithm was applied to intelligent robot for path planning in this study. First of all, combined with the basketball motion trajectory model, the sport recognition in basketball training was analyzed. Second, the mathematical model of the basketball motion trajectory of the shooting motion was established, and the factors affecting the shooting were analyzed. Thirdly, on this basis, the machine learning-based improved Q-Learning algorithm was proposed, the path planning of the moving robot was realized, and the obstacle avoidance behavior was accomplished effectively. In the path planning, the principle of fuzzy controller was applied, and the obstacle ultrasonic signals acquired around the robot were taken as input to effectively avoid obstacles. Finally, the robot was able to approach the target point while avoiding obstacles. The results of simulation experiment show that the obstacle avoidance path obtained by the improved Q-Learning algorithm is flatter, indicating that the algorithm is more suitable for the obstacle avoidance of the robot. Besides, it only takes about 250 s for the robot to find the obstacle avoidance path to the target state for the first time, which is far lower than the 700 s of the previous original algorithm. As a result, the fuzzy controller applied to the basketball robot can effectively avoid the obstacles in the robot movement process, and the motion trajectory curve obtained is relatively smooth. Therefore, the proposed machine learning algorithm has favorable obstacle avoidance effect when it is applied to path planning in basketball training, and can effectively prevent sports injuries in basketball activities.

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“…As these systems provide valuable data on the athlete’s performance in competition or training situations, they are of particular value for optimizing training and match preparation. Beyond gaining insight into individual performances, motion tracking can also be used to analyze team performances [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

Brumann

Kukuk

Reinsberger

2021

Sensors

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In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player’s feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario.

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Analyzing Basketball Movements and Pass Relationships Using Realtime Object Tracking Techniques Based on Deep Learning

Cited by 57 publications

References 32 publications

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

[Retracted] Development of Basketball Tactics Basic Cooperation Teaching System Based on CNN and BP Neural Network

Adoption of Machine Learning Algorithm-Based Intelligent Basketball Training Robot in Athlete Injury Prevention

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

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