| Wu Jin (吴进),Yang Xue,Xi Meng,Wan Xianghong.[J].高技术通讯(英文),2021,27(2):163~172 |
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| Research on behavior recognition algorithm based on SE-I3D-GRU network |
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| 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 Name | Affiliation | | 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: |
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| 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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