标题：
Human action recognition with 3D convolution skip-connections and RNNs
点击次数：
所属单位：
自动化学院
发表刊物：
Lect. Notes Comput. Sci.
摘要：
This paper proposes a novel network architecture for human action recognition. First, we employ a pre-trained spatio-temporal feature extractor to perform spatio-temporal features extraction on videos. Then, several-level spatio-temporal features are concatenated by 3D convolution skip-connections. Moreover, a batch normalization layer is applied to normalize the concatenated features. Subsequently, we feed these normalized features into a RNN architecture to model temporal dependencies, which enables our network to deal with long-term information. In addition, we divide each video into three parts in which each part is split into non-overlapping 16-frame clips to achieve data augmentation. Finally, the proposed method is evaluated on UCF101 Dataset and is compared with existing excellent methods. Experimental results demonstrate that our method achieves the highest recognition accuracy. © 2018, Springer Nature Switzerland AG.
ISSN号：
0302-9743
是否译文：
否
发表时间：
2018-01-01
合写作者：
Song, Jiarong,Zhang, Qiuyan,Fang, Ting,Hu, Guoxiong,Han, Jiaming,Chen, Cong
通讯作者：
Song, Jiarong,杨忠
发表时间：
2018-01-01
学位：工学博士学位
职称：教授
所在单位：自动化学院