Sports video athlete detection based on deep learning

Ren, Hao

doi:10.1007/s00521-022-07077-9

Cited by 7 publications

(5 citation statements)

References 22 publications

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“…Table 2 summarizes the characteristics of the included studies. Twenty-six articles were considered for assessing the effect of artificial intelligence on biomechanics of gait and sport functions, and eight articles related to the investigations deep learning [ 26 , 27 , 32 , 33 , 35 , 37 , 45 , 46 ], eleven studies on unsupervised learning [ 7 , 8 , 11 - 13 , 17 , 29 , 31 , 34 , 36 , 41 ], three studies on unsupervised learning [ 38 , 39 , 42 ], seven articles concentrated performance-prediction [ 1 , 7 , 8 , 28 , 31 , 34 , 41 ], six articles about accuracy of parameters [ 11 - 13 , 31 , 36 , 43 ], six studies on sports domain [ 7 , 8 , 11 , 30 , 31 , 41 ], and three articles on gait domain [ 40 , 43 , 44 ].…”

Section: Resultsmentioning

confidence: 99%

“…According to the findings, a CNN structure, which is employed in tandem with image representations, attains the most optimal outcomes. Hao Ren et al [ 27 ] developed a system for detecting sports video athletes using deep learning for experimental testing. Additionally, the accuracy of detection improved by nearly 10%, as compared to conventional convolution network recognition algorithms, which underscores the recognition advantages of the system.…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

2023

J Biomed Phys Eng

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Background: Artificial neural network helps humans in a wide range of activities, such as sports. Objective: This paper aims to investigate the effect of artificial intelligence on decision-making related to human gait and sports biomechanics, using computer-based software, and to investigate the impact of artificial intelligence on individuals’ biomechanics during gait and sports performance. Material and Methods: This review was conducted in compliance with the PRISMA guidelines. Abstracts and citations were identified through a search based on Science Direct, Google Scholar, PubMed, Elsevier, Springer Link, Web of Science, and Scopus search engines from 1995 up to 2023 to obtain relevant literature about the impact of artificial intelligence on biomechanics. A total of 1000 articles were found related to biomechanical characteristics of gait and sport and 26 articles were directly pertinent to the subject. Results: The extent of the application of artificial intelligence in sports biomechanics in various fields. In addition, various variables in the fields of kinematics, kinetics, and the field of time can be investigated based on artificial intelligence. Conventional computational techniques are limited by the inability to process data in its raw form. Artificial Intelligence (AI) and Machine Learning (ML) techniques can handle complex and high-dimensional data. Conclusion: The utilization of specialized systems and neural networks in gait analysis has shown great potential in sports performance analysis. Integrating AI into this field would be a significant advancement in sport biomechanics. Coaches and athletes can develop more precise training regimens with specialized performance prediction models.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

2023

J Biomed Phys Eng

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“…Ross et al (2023) [ 15 ] harnessed the power of deep learning-based image processing techniques to meticulously process and analyze surface feature images in titanium alloy machining processes, yielding precise measurements and evaluations of surface attributes. Ren (2023) [ 16 ] introduced an athlete detection methodology for sports videos anchored in deep learning principles. The research method facilitated accurate athlete identification within sports videos through the adept utilization of deep learning models.…”

Section: Literature Reviewmentioning

confidence: 99%

The intelligent football players’ motion recognition system based on convolutional neural network and big data

Wang,

Guo

2023

Heliyon

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“…Deep learning-based pose estimation models, such as convolutional neural networks (CNNs) or pose estimation networks like OpenPose, have shown promising results in accurately inferring human poses in real-time. By integrating action recognition and posture estimation techniques, applications can gain a deeper understanding of human behavior and movement patterns in various contexts, such as sports analysis, surveillance, human-computer interaction, and healthcare monitoring [3]. For instance, in sports analysis, these techniques can be used to track and analyze athletes' movements to provide insights into their performance and technique [4].…”

Section: Introductionmentioning

confidence: 99%

Athletes' Action Recognition and Posture Estimation Algorithm Based on Image Processing Technology

Cheng

2024

jes

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A posture estimation algorithm based on image processing technology involves analyzing images or video frames to determine the position and orientation of human bodies or specific body parts. This process typically begins with detecting key points or landmarks on the body, such as joints or anatomical features. This paper presents a novel approach for athletes' action recognition and posture estimation using image processing technology, specifically focusing on the Contour Mapping Multivariant Recognition and Estimation (CMMRE) algorithm. The primary objective is to develop a robust system capable of accurately identifying human actions and estimating postures from visual data, with potential applications in sports analysis, surveillance, and human-computer interaction. The CMMRE algorithm employs advanced techniques for contour mapping, feature extraction, and prediction, leveraging deep learning methodologies to analyze and interpret visual information effectively. Through a series of experiments and simulations, the algorithm's performance is evaluated, showcasing its ability to achieve high accuracy in action recognition tasks across various scenarios. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement. The algorithm achieved an average accuracy of 95% in recognizing walking and running actions, and 80% in identifying jumping actions. The results highlight the algorithm's strengths in accurately predicting actions and estimating postures, while also identifying areas for improvement.

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Sports video athlete detection based on deep learning

Cited by 7 publications

References 22 publications

Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review

The intelligent football players’ motion recognition system based on convolutional neural network and big data

Athletes' Action Recognition and Posture Estimation Algorithm Based on Image Processing Technology

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