Computer Science ›› 2022, Vol. 49 ›› Issue (6): 269-275.doi: 10.11896/jsjkx.210500070

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Multi-algorithm Fusion Behavior Classification Method for Body Bone Information Reconstruction

ZHAO Xiao-hu, YE Sheng, LI Xiao   

  1. National, Local Joint Engineering Laboratory of Internet Application Technology on Mine(China University of Mining, Technology), Xuzhou, Jiangsu 221008, China
    School of Information and Control Engineering,China University of Mining and Technology,Xuzhou,Jiangsu 221008,China
  • Received:2021-05-11 Revised:2021-09-06 Online:2022-06-15 Published:2022-06-08
  • About author:ZHAO Xiao-hu,born in 1976,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intelligence,mine informatization,key technology of mine Internet of Things transmission layer.
    YE Sheng,born in 1996,postgraduate.His main research interests include computer vision and so on.
  • Supported by:
    National Key R & D Program of China(2017YFC0804400).

Abstract: Aiming at the poor measurement effect of behavior monitoring in real life,a new method of extracting human behavior features is proposed.It not only considers the body point information,but also integrates the environmental attribute information of image.Considering that a large number of existing experiments use a variety of complex algorithms for experimental classification on the basis of human body features extraction,it does not take into account the irrationality of only using the body features for algorithm evaluation.Therefore,an image information reconstruction method based on body features is proposed in this paper,which combines the image convolution network of body features,attention mechanism and image recognition method to realize human behavior recognition.The body point information is extracted by Openpose,and then the body points are classified by graph convolution and attention.On the basis of the first classification,the body point expansion coefficient is added to segment the images so as to realize second accurate classification.Finally,the evaluation accuracy on the HMDB51 dataset improves by 5.6%,and it has a big advantage in the actual test.This shows that the method is not only more accurate,but also has more practical application value.

Key words: Action classification, Attention mechanism, Graph convolutional network, Image recognition, Transformer

CLC Number: 

  • TP391
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