scholarly journals Human Action Recognition Algorithm Based on Adaptive Initialization of Deep Learning Model Parameters and Support Vector Machine

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 59405-59421 ◽  
Author(s):  
Feng-Ping An
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Learning Model ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Model Parameters ◽  
Deep Learning Model

Multiple stream deep learning model for human action recognition

2020 ◽  
Vol 93 ◽  
pp. 103818 ◽  
Author(s):  
Ye Gu ◽  
Xiaofeng Ye ◽  
Weihua Sheng ◽  
Yongsheng Ou ◽  
Yongqiang Li
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Learning Model ◽  
Deep Learning Model

scholarly journals Extraction and Recognition Method of Basketball Players’ Dynamic Human Actions Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Qiulin Wang ◽  
Baole Tao ◽  
Fulei Han ◽  
Wenting Wei
Keyword(s):  
Deep Learning ◽  
Action Recognition ◽  
Recognition Accuracy ◽  
Human Action Recognition ◽  
Human Action ◽  
Recognition Algorithm ◽  
Basketball Players ◽  
Human Actions ◽  
Basketball Game ◽  
Wide Range

The extraction and recognition of human actions has always been a research hotspot in the field of state recognition. It has a wide range of application prospects in many fields. In sports, it can reduce the occurrence of accidental injuries and improve the training level of basketball players. How to extract effective features from the dynamic body movements of basketball players is of great significance. In order to improve the fairness of the basketball game, realize the accurate recognition of the athletes’ movements, and simultaneously improve the level of the athletes and regulate the movements of the athletes during training, this article uses deep learning to extract and recognize the movements of the basketball players. This paper implements human action recognition algorithm based on deep learning. This method automatically extracts image features through convolution kernels, which greatly improves the efficiency compared with traditional manual feature extraction methods. This method uses the deep convolutional neural network VGG model on the TensorFlow platform to extract and recognize human actions. On the Matlab platform, the KTH and Weizmann datasets are preprocessed to obtain the input image set. Then, the preprocessed dataset is used to train the model to obtain the optimal network model and corresponding data by testing the two datasets. Finally, the two datasets are analyzed in detail, and the specific cause of each action confusion is given. Simultaneously, the recognition accuracy and average recognition accuracy rates of each action category are calculated. The experimental results show that the human action recognition algorithm based on deep learning obtains a higher recognition accuracy rate.

Sign in / Sign up

Export Citation Format

Share Document