Research on static image recognition of sports based on machine learning

Guangjing, Li; Zhang, Cuiping

doi:10.3233/jifs-179203

Cited by 3 publications

(2 citation statements)

References 21 publications

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“…The accuracy of target recognition can be guaranteed, but it is undesirable to reduce the real-time performance of the algorithm. Although many research results have been achieved in the target recognition of medium-sized soccer robots, there are still some common problems in the research of vision applications, such as the poor real-time performance of the algorithm and the low recognition accuracy of the algorithm [14,15]. How to ensure the real-time performance, accuracy and anti-noise performance of the algorithm in a complex environment (lighting transformation, occlusion, high-speed motion) is a complex problem.…”

Section: Introductionmentioning

confidence: 99%

Retracted: Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

et al. 2020

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The purpose of this article is to identify football and related environmental variables through its VR images under the current situation where the vision system has become the only way for football robots to obtain the external environment, so as to improve the chances of winning the game. First of all, this article uses color filtering to enhance the football characteristics in the VR perspective, so as to distinguish the football from the off-field environment and shadows. The optimal threshold of the enhanced image is automatically determined by the Ostu method, so the image will not be affected by shadows during segmentation, and the contour of the image can be segmented. At the same time, using the humanoid medium-sized football game machine system as the platform, the relevant processing algorithms of the humanoid football robot front-view system are studied to realize the work of color image segmentation, edge extraction, straight line extraction, cross-line recognition and target post-recognition. PA-SIFT algorithm is used to quickly identify the graphics. Data verification results show that the recognition rate of the PA-SIFT algorithm can reach 96%, ensuring the real-time and feasibility of the algorithm. In addition, the divide-and-conquer algorithm and the related processing algorithm of the vision system are combined to determine the central area of the image, so that the algorithm is not affected by the external environment, and the algorithm is robust and can improve actual competition.

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Section: Introductionmentioning

confidence: 99%

Retracted: Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

et al. 2020

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“…Target detection technology is of great significance for sports scenes such as basketball, soccer, etc. The rich sensory functions and 3D display environment make VR an ideal video imaging tool [1][2]. When target detection is applied to modern sports teaching and sports training, its use of aerial images extracted from sports scenes can well display and analyze each technical action while allowing athletes to simulate the same training situation as the actual scene.…”

Section: Introductionmentioning

confidence: 99%

Development trend of the application of image recognition technology in the process of sports training

Wang

2024

Applied Mathematics and Nonlinear Sciences

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In the realm of sports training, the role of accurate image recognition is increasingly crucial for the effective correction of athletic movements. This research paper delves into the application of image recognition technologies to analyze sports training actions. Initial steps include the enhancement of image quality by filtering and sharpening images captured at a sports academy. Advanced techniques such as target detection algorithms and critical frame extraction are then applied to these refined images. Evaluations conducted on the KTH and UCF Sports action datasets reveal an average recognition rate of 88%, with further breakdowns indicating lower performance in activities like walking, jogging, and fast running in the KTH dataset. In contrast, uniform recognition results are observed in the UCF dataset with an average rate of 89.2% across various actions. The findings underscore the effectiveness of image recognition in improving sports training methodologies.

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Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Zhou

Wang

et al. 2023

Electronics

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Gesture recognition, as a core technology of human–computer interaction, has broad application prospects and brings new technical possibilities for smart homes, medical care, sports training, and other fields. Compared with the traditional human–computer interaction models based on PC use with keyboards and mice, gesture recognition-based human–computer interaction modes can transmit information more naturally, flexibly, and intuitively, which has become a research hotspot in the field of human–computer interaction in recent years. This paper described the current status of gesture recognition technology, summarized the principles and development history of electromagnetic wave sensor recognition, stress sensor recognition, electromyographic sensor recognition, and visual sensor recognition, and summarized the improvement of this technology by researchers in recent years through the direction of sensor structure, selection of characteristic signals, the algorithm of signal processing, etc. By sorting out and comparing the typical cases of the four implementations, the advantages and disadvantages of each implementation and the application scenarios were discussed from the two aspects of dataset size and accuracy. Based on the abovementioned discussion, the problems and challenges of current gesture recognition technology were discussed in terms of the biocompatibility of sensor structures, wearability and adaptability, stability, robustness, and crossover of signal acquisition and analysis algorithms, and the future development directions in this field were proposed.

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Research on static image recognition of sports based on machine learning

Cited by 3 publications

References 21 publications

Retracted: Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

Retracted: Fast Recognition Method of Football Robot’s Graphics From the VR Perspective

Development trend of the application of image recognition technology in the process of sports training

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

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