SVM-HMM Based Human Behavior Recognition

2015 ◽  
pp. 93-103 ◽  
Author(s):  
Shanshan Han ◽  
Minfei Zhang ◽  
Penglin Li ◽  
Jinjie Yao
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Human Behavior Recognition

3D Human behavior recognition based on binocular vision and face–hand feature

Optik ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4712-4717 ◽  
Author(s):  
Qing Ye ◽  
Junfeng Dong ◽  
Yongmei Zhang
Keyword(s):  
Human Behavior ◽  
Binocular Vision ◽  
Behavior Recognition ◽  
Human Behavior Recognition

Based on spatio-temporal graph convolution networks with residual connection for intelligence behavior recognition

2021 ◽  
pp. 002072092199660
Author(s):  
Yinong Zhang ◽  
Shanshan Guan ◽  
Cheng Xu ◽  
Hongzhe Liu
Keyword(s):  
Human Behavior ◽  
Behavior Recognition ◽  
Convolutional Network ◽  
Skeleton Model ◽  
Basic Technology ◽  
Temporal Model ◽  
Temporal Graph ◽  
Spatio Temporal ◽  
Human Behavior Recognition ◽  
Behavior Characteristics

In the era of intelligent education, human behavior recognition based on computer vision is an important branch of pattern recognition. Human behavior recognition is a basic technology in the fields of intelligent monitoring and human-computer interaction in education. The dynamic changes of human skeleton provide important information for the recognition of educational behavior. Traditional methods usually use manual information to label or traverse rules only, resulting in limited representation capabilities and poor generalization performance of the model. In this paper, a kind of dynamic skeleton model with residual is adopted—a spatio-temporal graph convolutional network based on residual connections, which not only overcomes the limitations of previous methods, but also can learn the spatio-temporal model from the skeleton data. In the big bone NTU-RGB + D dataset, the network model not only improved the representation ability of human behavior characteristics, but also improved the generalization ability, and achieved better recognition effect than the existing model. In addition, this paper also compares the results of behavior recognition on subsets of different joint points, and finds that spatial structure division have better effects.

Convolutional neural network for human behavior recognition based on smart bracelet

2020 ◽  
Vol 38 (5) ◽  
pp. 5615-5626
Author(s):  
Junsuo Qu ◽  
Ning Qiao ◽  
Haonan Shi ◽  
Chang Su ◽  
Abolfazl Razi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Behavior ◽  
Behavior Recognition ◽  
Human Behavior Recognition
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