Basketball Motion Posture Recognition Based on Recurrent Deep Learning Model

Liu, FeiPeng; Zhang, Wei

doi:10.1155/2022/8314777

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…e influence of the offensive decisionmaking process on 1V1 basketball subsystem is analyzed, which is related to the relationship between defensive position and angle. After the video performance, digital analysis was conducted to determine the position of the attacker's and defender's feet and the trajectory of the participant's evacuation movement [9]. e use of inertial sensors to collect hand movement data to identify hand movements has led to effective interaction between doctors and computers [10].…”

Section: Literature Reviewmentioning

confidence: 99%

A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence

Hou

Lian

et al. 2022

Mobile Information Systems

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To increase the widespread attention of human gesture recognition technology, this paper proposes a basketball pose recognition method based on unit action division. Initially, the human gesture recognition algorithm is introduced for the verification of various effects and gestures of basketball players by monitoring various actions of basketball and to obtain the data of limbs using different detectors for different basketball movements. A large amount of data collection work was carried out for the experiment, and the corresponding experimental scenarios were described in the experimental design for testing. The methods for data processing and data division presented in this work are used to process the collected data. A feature vector set that describes a particular action is acquired and used as a sample set. The sample set is then delivered to the classifier. The classifier is implemented here based on the already-existing Weka platform, and performance evaluation and analysis of various classifiers are implemented. The results show that the differential limb function category has a better recognition effect on BP. The average accuracy of upper limb function was 92.19%, the average recall rate was 92.19%, and the accuracy of lower limb was much higher. The average accuracy of the four algorithms was within the range of 96.99% to 99.19% for lower limb movements and 84.89% to 92.19% for upper limb movements. The BP prosthetic network is used to create separate classifiers, ensuring that each basketball move was more than 95% accurate and that the average accuracy per basketball move was much more accurate. As a result, the accuracy level reached up to 98.85%. The validity of the basketball gesture recognition method recognized by the authors is sufficient and reasonable.

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Section: Literature Reviewmentioning

confidence: 99%

A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence

Hou

Lian

et al. 2022

Mobile Information Systems

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“…For example, "hand" objects in digital images are recognized through the color of the skin and the shape of the hand. These recognition techniques are limited by feature rules that can be summarized manually [21]. Compiling rule sets is challenging for objects with complex rules.…”

Section: Introductionmentioning

confidence: 99%

High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

Ding

2023

Applied Sciences

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Pose recognition in character animations is an important avenue of research in computer graphics. However, the current use of traditional artificial intelligence algorithms to recognize animation gestures faces hurdles such as low accuracy and speed. Therefore, to overcome the above problems, this paper proposes a real-time 3D pose recognition system, which includes both facial and body poses, based on deep convolutional neural networks and further designs a single-purpose 3D pose estimation system. First, we transformed the human pose extracted from the input image to an abstract pose data structure. Subsequently, we generated the required character animation at runtime based on the transformed dataset. This challenges the conventional concept of monocular 3D pose estimation, which is extremely difficult to achieve. It can also achieve real-time running speed at a resolution of 384 fps. The proposed method was used to identify multiple-character animation using multiple datasets (Microsoft COCO 2014, CMU Panoptic, Human3.6M, and JTA). The results indicated that the improved algorithm improved the recognition accuracy and performance by approximately 3.5% and 8–10 times, respectively, which is significantly superior to other classic algorithms. Furthermore, we tested the proposed system on multiple pose-recognition datasets. The 3D attitude estimation system speed can reach 24 fps with an error of 100 mm, which is considerably less than that of the 2D attitude estimation system with a speed of 60 fps. The pose recognition based on deep learning proposed in this study yielded surprisingly superior performance, proving that the use of deep-learning technology for image recognition has great potential.

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Real-Time Recognition Method of Video Basketball Technical Action Based on Target Detection Algorithm

Zhu

2022

Mathematical Problems in Engineering

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Aiming at the problems of poor real-time recognition efficiency and low recognition accuracy of video basketball technical movements, a real-time recognition method of video basketball technical movements based on the target detection algorithm is proposed. This study firstly collects video basketball technical action images and detects their edge contour features. Through machine vision block template matching processing, the video basketball technology motion capture results are output. Then, according to the target probability of video basketball technical action detection, the basketball video sample set is estimated and constructed, the sequence of basketball video is obtained, and the basketball video target detection algorithm is designed. Finally, the similarity between the two vectors of video basketball technical action targets is measured to realize real-time recognition of video basketball technical actions. The experimental results show that the method in this study has high accuracy in extracting feature points of basketball video image decomposition, the real-time recognition time of video basketball technical action is up to 4s, and the recognition error of basketball rotation projection technology is small, which is close to the actual value.

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Basketball Motion Posture Recognition Based on Recurrent Deep Learning Model

Cited by 3 publications

References 13 publications

A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence

A Basketball Training Posture Monitoring Algorithm Based on Machine Learning and Artificial Intelligence

High Speed and Accuracy of Animation 3D Pose Recognition Based on an Improved Deep Convolution Neural Network

Real-Time Recognition Method of Video Basketball Technical Action Based on Target Detection Algorithm

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