Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model

Deng, Huijian; Shi-jian, Cao; Tang, Jingen

doi:10.1155/2022/2479939

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…The presence of these noises makes it difficult to identify vehicle targets. The main role of the image before working module is to filter and strip the image [17].…”

Section: Systemmentioning

confidence: 99%

The Application and Development Trend of Youth Sports Simulation Based on Computer Vision

Zhou

Wang

2022

Wireless Communications and Mobile Computing

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Based on computer vision technology, this paper presents a human motion analysis and target tracking technology based on computer vision. In terms of moving target detection, the current moving target detection technology is summarized, and some experimental results of the algorithm are given. The background difference method under monocular camera is emphatically analyzed. The preliminary human contour is obtained by the background difference method. In order to obtain a smoother target contour, the mathematical morphology is used to remove the noise, and the judgment algorithm of the size of the image connected domain is added. A specific threshold is set to remove the connected domain of the noise block less than the threshold. In the aspect of human motion recognition, this paper selects human motion features, including minimum external rectangle aspect ratio, rectangularity, circularity, and moment invariant. The criteria for selecting human motion features are strong noise resistance and obvious distinction. Then, the three types of human motion images are classified and recognized. After cross-validation and parameter optimization, the recognition accuracy is significantly improved. The experimental results show that the video sequence collected in the field has a total of 376 frames, and the frame rate is 10 frames/s. Due to the small traffic, the mean shift algorithm based on adaptive feature fusion is used to track the target every 2-3 frames. And set the inverse X direction as the direction of entering the scene and the X direction as the direction of moving out of the scene so that the allowable error of the distance between the detection and tracking results is 10. The weight of each feature is dynamically updated by the similarity between the candidate model and the target model, which solves the problem that the mean shift algorithm is not robust enough when similar objects are occluded and interfered and achieves more accurate tracking.

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“…The presence of these noises makes it difficult to identify vehicle targets. The main role of the image before working module is to filter and strip the image [17].…”

Section: Systemmentioning

confidence: 99%

The Application and Development Trend of Youth Sports Simulation Based on Computer Vision

Zhou

Wang

2022

Wireless Communications and Mobile Computing

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“…The aggression shown by the players during sports can result in some serious situations and can affect the quality of sports. Deng et al [23] have presented a system grounded on the swarm intelligence and the employment of the Internet of Things for the prediction of aggressive behavior from the players. After the recognition of various emotions based on the proposed model, the related personnel can act in time and resolve the issue very conveniently.…”

Section: Dss For Action Recognition Of Track and Field Sports Using Acomentioning

confidence: 99%

Decision support system for effective action recognition of track and field sports using ant colony optimization

He¹,

Ren²,

Cheng³

2023

Soft Comput

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Based on emerging technologies like arti cial intelligence, machine learning, the Internet of Things, and virtual reality, various Decision Support Systems (DSS) are being employed for the revolution in the sports industry. The coach can now make very precise and unbiased decisions related to the players' skills and selection. It is now very convenient to improve the skills and performance of the players through the implementation of various computer-grounded methodologies. Professionals can recognize the unwanted behavior of players in time during sports and hence can ensure a peaceful atmosphere during sports. The recognition of non-standard actions by the players can help in the avoidance of serious injuries or illness. The DSS can predict the nature of the weather and the sports personnel can take decisions regarding the carrying out of games. The players can do their training without any restrictions on space and time. The real-time analysis of already existing videos of games can help the newcomers learn and improve their skills and performance. The trainers can check the physical tness of the sportsmen very e ciently and provide them with useful and valuable recommendations related to their tness level. The proposed study has used the Ant Colony Optimization to recognize and track the optimal features of athletes to enhance individual as well as team performance in sport competitions.

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“…The characteristic of Butterworth filter can be reflected by its gain [11]: Among them, GðωÞ is the filter gain. Compared with traditional motion recognition using contact sensors (such as threedimensional acceleration sensors, electromyographic signal sensors, etc.…”

Section: Research Plan For Intelligent Recognition Ofmentioning

confidence: 99%

Intelligent Recognition System of Sports Athletes’ Wrong Actions Based on AI+IoT

Zhang

2022

Wireless Communications and Mobile Computing

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How to effectively identify errors in athletes' movements, and then achieve the goal of correcting or eliminating them, is a problem that the sports world is currently trying to solve. Sports field is a field where sensor-based human movement recognition can play a role. Obtaining human body data through sensors can provide data basis for research on the characteristics of human movement, improvement of sports level, and technical movement analysis of sports. The main research direction of this paper is the AI+IoT joint recognition of human movements. The target human movements involved include standing, walking, running, shooting, jump shot, jumping, dribbling, walking dribbling, and running dribbling. Computer vision, as a branch of the artificial intelligence research field, uses computers to simulate human visual cognitive ability, excavate useful information from digitized images or videos without manual intervention, and strive to achieve human understanding of visual signals, and realize the ability to understand visual signals from a low level. Conversion of data input to high-level knowledge output. The system scoring module is the core functional module of the system. It extracts the angle feature of the trainer's skeleton data, finds the corresponding frame to correspond, and prompts the trainer's current standard degree of movement in the form of score. In terms of judgment accuracy, the accuracy of all subjects after watching the complete video is higher than the accuracy of the time blocking point, with extremely significant differences, r = -10.80, df =52, p < 0:001. This research improves the universality of target detection and extraction in the real natural environment of the machine vision system and improves the complexity and robustness of human action recognition.

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Prediction of Sports Aggression Behavior and Analysis of Sports Intervention Based on Swarm Intelligence Model

Cited by 4 publications

References 23 publications

The Application and Development Trend of Youth Sports Simulation Based on Computer Vision

The Application and Development Trend of Youth Sports Simulation Based on Computer Vision

Decision support system for effective action recognition of track and field sports using ant colony optimization

Intelligent Recognition System of Sports Athletes’ Wrong Actions Based on AI+IoT

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