Motion capture and evaluation system of football special teaching in colleges and universities based on deep learning

Yin, Xiaohui; Vignesh, C. Chandru; Vadivel, Thanjai

doi:10.1007/s13198-021-01557-2

Cited by 8 publications

(5 citation statements)

References 31 publications

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“…With the rapid development of DL, its advantages in football sports analysis are increasingly evident. Yin et al (2022) proposed a DL-assisted motion capture system to reduce animation costs and improve motion capture data for football matches through bidirectional motion analysis. Simulation analysis showed that this framework was highly reliable [16].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Yin et al (2022) proposed a DL-assisted motion capture system to reduce animation costs and improve motion capture data for football matches through bidirectional motion analysis. Simulation analysis showed that this framework was highly reliable [16]. Zanganeh et al (2022) focused on computer vision analysis of football videos and introduced a public dataset called IAUFD to address the issue of insufficient data for DL methods.…”

Section: Literature Reviewmentioning

confidence: 99%

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The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

Mou

2024

IEEE Access

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This work proposes a novel Class Aware Network (CANet) for analyzing football player performance by decoding their body movements. Firstly, the role of the Internet of Things in football sports analysis and the advantages of deep learning techniques are introduced. Secondly, pyramid pooling modules and attention mechanisms are introduced. Moreover, the Group-split-bottleneck (GS-bt) module is employed, and the CANet is designed to extract and utilize multi-scale feature information and enhance the network's ability to perceive details. Finally, the effectiveness of the proposed model is validated through comparisons with other models. The results show that in image classification experiments, the mean accuracy of the GSbt module is at least 2.79% higher than that of other models. In human body parsing experiments, results from two different datasets demonstrate that the CANet model achieves the highest mean Intersection over Union, improving by at least 6.02% compared to other models. These findings indicate that the proposed CANet model performs better in image classification and human body parsing tasks, presenting higher accuracy and generalization capabilities. This work provides new methods and technologies for analyzing football player performance, potentially promoting sports development and application in athletics.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

Mou

2024

IEEE Access

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“…Te connection between the key points represents the rigid body. When they are "shape unchanged," the connections between key points represent rigid bodies [20]. Te main research on movement posture is to build a database of human movement postures based on the head, body, hips, and limbs.…”

Section: Establish Motion Model Databasementioning

confidence: 99%

Research on Dance Motion Capture Technology for Visualization Requirements

Sun

2022

Scientific Programming

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“Motion capture technology” refers to the use of optical sensing equipment to record the dancer’s movement trajectory during dance and then convert this movement trajectory into applicable data information in animation software. In the 3D animation software, the corresponding dancer model can be constructed, and the matching costumes can also be designed. After combining it with the motion capture data, animation and dance data can be generated. In this way, virtual simulation software can create some virtual visualization scenes and present more diverse and complex demonstration effects. This article focuses on three aspects: “an overview of the connotation of motion capture technology,” “design of virtual dance visualization scene based on motion capture technology,” and “application of virtual dance visualization scene based on motion capture technology.”

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“…The combined teaching methods are as follows. When teaching football in colleges and universities, especially due to changes in the teaching environment and student learning methods, traditional teaching methods are difficult to provide students with the best educational services, thereby increasing the influence of teaching [6]. The rapid maturity of Internet technology, computer technology and information technology has enabled the positive development and progress of network-based distance learning systems [7].…”

Section: Introductionmentioning

confidence: 99%

Intelligent football teaching based on virtual simulation and robust similarity measurement algorithm

Zhao

Zhang²

2023

Preprint

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The computer virtual simulation teaching system is an integrated computer teaching system that uses network technology and virtual reality technology to simulate real-world experiments. The system is a computer laboratory used for teaching. Through the virtual simulation teaching system, the advantages of network teaching can be combined, and it has the characteristics of fast construction speed, low teaching cost, and overcoming various limitations and challenges. The current robust similarity measurement algorithm is based on a similarity measurement, which corresponds to tracking the appearance and alignment of the twin network search graphs. The sample target expression is constructed based on the data in the first structure, and then the target sample and test pattern are calculated through the active analogy of the measurement algorithm. Choose the most suitable location for the target result to set the test result. The use of integrated teaching technology in football sports technology is significantly better than traditional teaching methods. Comprehensive teaching can not only show detailed technical activities, but also expand students' learning space and time, which is essential for improving the level of professional football. According to the online application system for intelligent teaching, all system operations are performed in collaboration with the user's browser and application server, and the database system organizes and protects data related to the system. According to the network connection, the service will be used for intelligent teaching.

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Motion capture and evaluation system of football special teaching in colleges and universities based on deep learning

Cited by 8 publications

References 31 publications

The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

The Attention Mechanism Performance Analysis for Football Players Using the Internet of Things and Deep Learning

Research on Dance Motion Capture Technology for Visualization Requirements

Intelligent football teaching based on virtual simulation and robust similarity measurement algorithm

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