Human Action Pattern Recognition and Semantic Research Based on Embodied Cognition Theory

2019 ◽  
Vol 34 (05) ◽  
pp. 2055013 ◽  
Author(s):  
Liang Tan
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Video Surveillance ◽  
Human Behavior ◽  
Human Body ◽  
Body Motion ◽  
Behavior Recognition ◽  
Video Images ◽  
Human Behavior Recognition ◽  
Significant Region

Human body motion pattern recognition in video images is an important research direction in the field of pattern recognition. It has a very broad application prospect in many fields such as intelligent video surveillance, human-computer interaction, motion analysis, video retrieval, etc. Research has also received extensive attention from scholars at home and abroad. Pattern recognition is essentially a branch of artificial intelligence. It has its unique role in the field of artificial intelligence. Accurate recognition of human body motion patterns in video images is of great help in image classification, retrieval, human tracking and video surveillance. Based on the human visual perception mechanism, this paper proposes a human behavior recognition algorithm based on semantic saliency map. Through the combination of sliding window and similarity measure, the behavioral region that best exhibits the semantic features of the image is found, which is the semantically significant region. The semantic significant region and the original image are used as the dual input source to study the human behavior recognition, and the image is enhanced. The utilization of significant regional information better reveals the identifiable area of the image and contributes to the recognition of human behavior.

Research on Human Behavior Recognition in Video Surveillance

2011 ◽  
Vol 255-260 ◽  
pp. 2276-2280
Author(s):  
Wen Qiu Zhu ◽  
Qian Qian Li ◽  
Jun Feng Man ◽  
Xiang Bing Wen
Keyword(s):  
Knowledge Base ◽  
Video Surveillance ◽  
Human Behavior ◽  
Dirichlet Process ◽  
Human Motion ◽  
Viterbi Decoding ◽  
Hierarchical Dirichlet Process ◽  
Behavior Recognition ◽  
Human Behavior Recognition ◽  
Current Behavior

For effectively solving human behavior recognition in video surveillance, a novel behavior recognition model is presented. New behaviors may be produced in the process of human motion, hierarchical Dirichlet process is used to cluster monitored feature data of human body to decide whether unknown behaviors occur or not. The infinite hidden Markov model is used to learn unknown behavior patterns with supervised method, and then update the knowledge base. When knowledge base reaches a certain scale, the system can analyze human behaviors with unsupervised method. The Viterbi decoding algorithm of HMM is adopted to analyze current behavior of the human motion. The simulation experiments show that this method has unique advantage over others.

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.

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