文章摘要
Wu Jin (吴进),Yang Xue,Xi Meng,Wan Xianghong.[J].高技术通讯(英文),2021,27(2):163~172
Research on behavior recognition algorithm based on SE-I3D-GRU network
  
DOI:10.3772/j.issn.1006-6748.2021.02.007
中文关键词: 
英文关键词: behavior recognition, squeeze-and-excitation network (SENet), Incepton network, gated recurrent unit (GRU)
基金项目:
Author NameAffiliation
Wu Jin (吴进) (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) 
Yang Xue (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) 
Xi Meng (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) 
Wan Xianghong (School of Electronic and Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) 
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中文摘要:
      
英文摘要:
      In order to effectively solve the problems of low accuracy and large amount of calculation of current human behavior recognition, a behavior recognition algorithm based on squeeze-and-excitation network (SENet) combined with 3D Inception network (I3D) and gated recurrent unit (GRU) network is proposed. The algorithm first expands the Inception module to three-dimensional, and builds a network based on the three-dimensional module, and expands SENet to three-dimensional, making it an attention mechanism that can pay attention to the three-dimensional channel. Then SENet is introduced into the I3D network, named SE-I3D, and SENet is introduced into the GRU network, named SE-GRU. And, SE-I3D and SE-GRU are merged, named SE-I3D-GRU. Finally, the network uses Softmax to classify the results in the UCF-101 dataset. The experimental results show that the SE-I3D-GRU network achieves a recognition rate of 93.2% on the UCF-101 dataset.
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