scholarly journals Research on Sports Training Action Recognition Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Wang
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Human Motion ◽  
Medical Monitoring ◽  
Human Beings ◽  
Sports Training ◽  
Motion Recognition ◽  
Human Motion Recognition

With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

scholarly journals Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8 ◽  
Author(s):  
Zhongxiao Wang
Keyword(s):  
Deep Learning ◽  
Physical Training ◽  
Evaluation System ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Sports Training ◽  
Deep Learning Algorithm ◽  
The Impact ◽  
Physical Index

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.

The Research of 3D Human Motion Simulation and Video Analysis System Implemented in Sports Training

2014 ◽  
Vol 926-930 ◽  
pp. 2743-2746 ◽  
Author(s):  
Rui Min Hu ◽  
Zhen Dong He ◽  
Feng Bai
Keyword(s):  
Computer Vision ◽  
Video Analysis ◽  
Rapid Development ◽  
Human Movement ◽  
Research Direction ◽  
Human Motion ◽  
Motion Simulation ◽  
Sports Training ◽  
Camera Tracking ◽  
Human Motion Simulation

With the rapid development of computer technology, human motion tracking based on video is a kind of using ordinary camera tracking unmarked human movement technology. It has important application value in automatic monitoring, human-computer interaction, sports analysis and many other fields. This research is a hot research direction in the field of computer vision in recent years. Because of the complexity of the problem and the lack of understanding of the nature of the human visual tracking based on video is always a difficult problem in computer vision. The research content of this article is set in sports training, for motion analysis of non-contact, no interfere with measurement and simulation requirements, the use of computer graphics and computer vision technology, discussing 3D human motion simulation technology based on video analysis.

scholarly journals The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ning Feng ◽  
Ping Gao
Keyword(s):  
Motion Tracking ◽  
Moving Objects ◽  
Rapid Development ◽  
Human Motion ◽  
Motion Recognition ◽  
Snake Model ◽  
Running Time ◽  
Sports Science ◽  
Human Motion Recognition ◽  
Image Contour

With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.

scholarly journals RGB-D-based human motion recognition with deep learning: A survey

2018 ◽  
Vol 171 ◽  
pp. 118-139 ◽  
Author(s):  
Pichao Wang ◽  
Wanqing Li ◽  
Philip Ogunbona ◽  
Jun Wan ◽  
Sergio Escalera
Keyword(s):  
Deep Learning ◽  
Human Motion ◽  
Motion Recognition ◽  
Human Motion Recognition

A systematic Approach to Human Motion Recognition using Deep Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 1996-2005
Keyword(s):  
Deep Learning ◽  
Motion Analysis ◽  
Systematic Approach ◽  
Performance Metrics ◽  
Modern Technology ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Motion Recognition ◽  
Motion Data ◽  
Human Motion Recognition

The application of Human Motion Analysis (HMA) under Computer Vision (CV) is an emerging field which entails various applications such as gait analysis, behavioural cloning and animation of motion, intent detection, etc. For such motion analysis various open source datasets have been created that help analyze motion behaviour. Motion Capture (mocap) files have been used extensively to store motion data and analyze them. Although the weightage of these applications can be huge in modern technology, not much work on human motion recognition has been done using mocap datasets. In this paper, we propose a systematic approach to human motion recognition using software engineering, data analysis and deep learning algorithms. A Deep Learning (DL) model using Gated Recurrent Network (GRU) for the classification of human motion. CMU mocap dataset is used for analyzing motion data and modelling the DL framework. The trained algorithm is tested using accuracy and Mean Absolute Error (MAE) and a user live feed as performance metrics. A 90.1% validation accuracy is obtained on final evaluation.

scholarly journals Computer Vision for Human-Computer Interaction Using Noninvasive Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Janarthanan Ramadoss ◽  
J. Venkatesh ◽  
Shubham Joshi ◽  
Piyush Kumar Shukla ◽  
Sajjad Shaukat Jamal ◽  
...  
Keyword(s):  
Computer Vision ◽  
Human Computer Interaction ◽  
Target Identification ◽  
Body Movement ◽  
Human Motion ◽  
Human Hand ◽  
Motion Recognition ◽  
Human Motion Recognition ◽  
The Impact ◽  
Computer Interaction

Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

scholarly journals Computer Vision-Driven Evaluation System for Assisted Decision-Making in Sports Training

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Lijin Zhu
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Evaluation System ◽  
Rapid Development ◽  
Sports Training ◽  
Traditional System ◽  
System User ◽  
Overall Evaluation ◽  
Multiple Sensor ◽  
Application Fields

Computer vision has become a fast-developing technology in the field of artificial intelligence, and its application fields are also expanding, thanks to the rapid development of deep learning. It will be of great practical value if it is combined with sports. When a traditional exercise assistance system is introduced into sports training, the athlete’s training information can be obtained by monitoring the exercise process through sensors and other equipment, which can assist the athlete in retrospectively analyzing the technical actions. However, the traditional system must be equipped with multiple sensor devices, and the exercise information provided must be accurate. This paper proposes a motion assistance evaluation system based on deep learning algorithms for human posture recognition. The system is divided into three sections: a standard motion database, auxiliary instruction, and overall evaluation. The standard motion database can be customized by the system user, and the auxiliary teaching system can be integrated. The user’s actions are compared to the standard actions and intuitively displayed to the trainers as data. The system’s overall evaluation component can recognize and display video files, giving trainers an intelligent training platform. Simulator tests are also available. It also demonstrates the efficacy of the algorithm used in this paper.

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