Deep Learning Video Action Recognition Method Based on Key Frame Algorithm

2020 ◽  
pp. 62-73
Author(s):  
Li Tan ◽  
Yanyan Song ◽  
Zihao Ma ◽  
Xinyue Lv ◽  
Xu Dong
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Method ◽  
Key Frame

scholarly journals Construction and Simulation of Athlete’s Wrong Action Recognition Model in Sports Training Based on Embedded Wireless Communication and Computer Vision

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Naichun Gao
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Action Recognition ◽  
Center Of Gravity ◽  
Knee Valgus ◽  
Sports Training ◽  
Recognition Method ◽  
Total Frequency ◽  
Emg Signal ◽  
Computer Vision Technology

Embedded networking has a broad prospect. Because of the Internet and the rapid development of PC skills, computer vision technology has a wide range of applications in many fields, especially the importance of identifying wrong movements in sports training. To study the computer vision technology to identify the wrong movement of athletes in sports training, in this paper, a hidden Markov model based on computer vision technology is constructed to collect video and identify the landing and take-off movements and badminton serving movements of a team of athletes under the condition of sports training, Bayesian classification algorithm to analyze the acquired sports training action data, obtain the error frequency, and the number of errors of the landing jump action, and the three characteristic data of the displacement, velocity, and acceleration of the body’s center of gravity of the athlete in the two cases of successful and incorrect badminton serve actions and compared and analyzed the accuracy of the action recognition method used in this article, the action recognition method based on deep learning and the action recognition method based on EMG signal under 30 experiments. The training process of deep learning is specifically split into two stages: 1st, a monolayer neuron is built layer by layer so that the network is trained one layer at a time; when all layers are fully trained, a tuning is performed using a wake-sleep operation. The final result shows that the frequency of the wrong actions of the athletes on the landing jump is concentrated in the knee valgus, the total frequency of error has reached 58%, and the frequency of personal error has reached 45%; the problem of the landing distance of the two feet of the team athletes also appeared more frequently, the total frequency reached 50%, and the personal frequency reached 30%. Therefore, athletes should pay more attention to the problems of knee valgus and the distance between feet when performing landing jumps; the difference in the displacement, speed, and acceleration of the body’s center of gravity during the badminton serve will affect the error of the action. And the action recognition method used in this study has certain advantages compared with the other two action recognition methods, and the accuracy of action recognition is higher.

Key frame and skeleton extraction for deep learning-based human action recognition

2021 ◽  
Author(s):  
Hai-Hong Phan ◽  
Trung Tin Nguyen ◽  
Ngo Huu Phuc ◽  
Nguyen Huu Nhan ◽  
Do Minh Hieu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Skeleton Extraction ◽  
Key Frame

scholarly journals Application of Convolution Neural Network (CNN) Model Combined with Pyramid Algorithm in Aerobics Action Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qi Liang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Action Recognition ◽  
Network Model ◽  
Neural Network Model ◽  
Convolution Neural Network ◽  
Recognition Method ◽  
Recognition Model ◽  
Recognition Function ◽  
The Common

In order to realize high-accuracy recognition of aerobics actions, a highly applicable deep learning model and faster data processing methods are required. Therefore, it is a major difficulty in the field of research on aerobics action recognition. Based on this, this paper studies the application of the convolution neural network (CNN) model combined with the pyramid algorithm in aerobics action recognition. Firstly, the basic architecture of the convolution neural network model based on the pyramid algorithm is proposed. Combined with the application strategy of the common recognition model in aerobics action recognition, the traditional aerobics action capture information is processed. Through the characteristics of different aerobics actions, different accurate recognition is realized, and then, the error of the recognition model is evaluated. Secondly, the composite recognition function of the convolution neural network model in this application is constructed, and the common data layer effect recognition method is used in the optimization recognition. Aiming at the shortcomings of the composite recognition function, the pyramid algorithm is used to improve the convolution neural network recognition model by deep learning optimization. Finally, through the effectiveness comparison experiment, the results show that the convolution neural network model based on the pyramid algorithm is more efficient than the conventional recognition method in aerobics action recognition.

Fight Action Recognition Method using AI Deep Learning

2021 ◽  
Vol 27 (7) ◽  
pp. 482-489
Author(s):  
Ho-yeon Kim ◽  
Young-Ki Song ◽  
Jae-Soo Cho
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Method

Action recognition method of spatio-temporal feature fusion deep learning network

2018 ◽  
Vol 47 (2) ◽  
pp. 203007
Author(s):  
裴晓敏 Pei Xiaomin ◽  
范慧杰 Fan Huijie ◽  
唐延东 Tang Yandong
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Feature Fusion ◽  
Recognition Method ◽  
Learning Network ◽  
Spatio Temporal ◽  
Deep Learning Network ◽  
Temporal Feature

Deep Learning for Human Action Recognition Survey

2018 ◽  
Vol 6 (10) ◽  
pp. 323-328
Author(s):  
K.Kiruba . ◽  
D. Shiloah Elizabeth ◽  
C Sunil Retmin Raj
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action

scholarly journals Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226324-226336
Author(s):  
Shuguang Ning ◽  
Yigang He ◽  
Lifen Yuan ◽  
Yuan Huang ◽  
Shudong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Wireless Channel ◽  
Scene Recognition ◽  
Recognition Method
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