Through-the-Wall Human Behavior Recognition Algorithm with Commercial Wi-Fi Devices

Through the recognition and analysis of human motion information, the actual motion state of human body can be obtained. However, the multifeature fusion of human behavior has limitations in recognition accuracy and robustness. Combined with deep reinforcement learning, multifeature fusion human behavior recognition is studied and we proposed a multifeature fusion human behavior recognition algorithm using deep reinforcement learning. Firstly, several typical human behavior data sets are selected as the research data in the benchmark data set. In the selected data sets, the behavior category contained in each video is the same behavior, and there are category tags. Secondly, the attention model is constructed. In the deep reinforcement learning network, the small sampling area is used as the model input. Finally, the corresponding position of the next visual area is estimated according to the time series information obtained after the input. The human behavior recognition algorithm based on deep reinforcement learning multifeature fusion is completed. The results show that the average accuracy of multifeature fusion of the algorithm is about 95%, the human behavior recognition effect is good, the identification accuracy rate is as high as about 98% and passed the camera movement impact performance test and the algorithm robustness, and the average time consumption of the algorithm is only 12.7 s, which shows that the algorithm has very broad application prospects.

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

Optik ◽

10.1016/j.ijleo.2015.08.103 ◽

2015 ◽

Vol 126 (23) ◽

pp. 4712-4717 ◽

Cited By ~ 3

Author(s):

Qing Ye ◽

Junfeng Dong ◽

Yongmei Zhang

Keyword(s):

Human Behavior ◽

Binocular Vision ◽

Behavior Recognition ◽

Human Behavior Recognition

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Human behavior recognition for cooking support robot

RO-MAN 2004. 13th IEEE International Workshop on Robot and Human Interactive Communication (IEEE Catalog No.04TH8759) ◽

10.1109/roman.2004.1374787 ◽

2005 ◽

Cited By ~ 4

Author(s):

T. Fukuda ◽

Y. Nakauchi ◽

K. Noguchi ◽

T. Matsubara

Keyword(s):

Human Behavior ◽

Behavior Recognition ◽

Human Behavior Recognition

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

International Journal of Electrical Engineering Education ◽

10.1177/0020720921996600 ◽

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.

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