吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (4): 1244-1252.doi: 10.13229/j.cnki.jdxbgxb201704033

• Orginal Article • Previous Articles     Next Articles

Multi-level image sequences and convolutional neural networks based human action recognition method

MA Miao, LI Yi-bin   

  1. College of Control Science and Engineering, Shandong University, Ji'nan 250061,China
  • Received:2016-03-24 Online:2017-07-20 Published:2017-07-20

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Abstract: A multi-level image sequences and convolutional neural networks human action recognition method is proposed. First, a four-level image sequence structure is constructed, which is able to obtain richer information of human actions. Then the four-level image sequences are processed by convolutional neural networks. This structure is able to use appearance, motion, foreground and background information more sufficiently. Besides, a decomposition method of video sequence is proposed, which is able to acquire more detailed human activity information. This method decomposes each level sequence into sub-sequences, and represents actions from coarse to fine, thus, achieving more representative human activity features. The efficiency of the proposed method is verified by two challenging human action databases. The experiment results show that the proposed method improves the action recognition accuracy efficiently.

Key words: artificial intelligence, action recognition, video understanding, convolutional neural network

CLC Number: 

  • TP183
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