Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks.
文献类型：期刊论文
| 作者 | Wang, Lei; Xu, Yangyang; Cheng, Jun; Xia, Haiying; Yin, Jianqin; Wu, Jiaji |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2018 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Human action recognition is one of the fundamental challenges in robotics systems. In this paper, we propose one lightweight action recognitionarchitecture based on deep neural networks just using RGB data. The proposed architecture consists of convolution neural network (CNN), long short-term memory (LSTM) units, and temporal-wise attention model. First, the CNN is used to extract spatial features to distinguish objects from the background withboth local and semantic characteristics. Second, two kinds of LSTM networks are performed on the spatial feature maps of different CNN layers (pooling layer and fully-connected layer) to extract temporal motion features. Then, one temporal-wise attention model is designed after the LSTM to learn which parts in which frames are more important. Lastly, a joint optimization module is designed to explore intrinsic relations between two kinds of LSTM features. Experimental results demonstrate the efficiency of the proposed method. |
| URL标识 | 查看原文 |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/13568]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 推荐引用方式 GB/T 7714 | Wang, Lei,Xu, Yangyang,Cheng, Jun,et al. Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks.[J]. IEEE ACCESS,2018. |
| APA | Wang, Lei,Xu, Yangyang,Cheng, Jun,Xia, Haiying,Yin, Jianqin,&Wu, Jiaji.(2018).Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks..IEEE ACCESS. |
| MLA | Wang, Lei,et al."Human Action Recognition by Learning Spatio-Temporal Features With Deep Neural Networks.".IEEE ACCESS (2018). |
入库方式： OAI收割
来源：深圳先进技术研究院
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