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Video-Based Human Action Recognition Using Spatial Pyramid Pooling and 3D Densely Convolutional Networks
| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Wanli Yang Yimin Chen Chen Huang Mingke Gao |
| Abstract | In recent years, the application of deep neural networks to human behavior recognition has become a hot topic. Although remarkable achievements have been made in the field of image recognition, there are still many problems to be solved in the area of video. It is well known that convolutional neural networks require a fixed size image input, which not only limits the network structure but also affects the recognition accuracy. Although this problem has been solved in the field of images, it has not yet been broken through in the field of video. To address the input problem of fixed size video frames in video recognition, we propose a three-dimensional (3D) densely connected convolutional network based on spatial pyramid pooling (3D-DenseNet-SPP). As the name implies, the network structure is mainly composed of three parts: 3DCNN, DenseNet, and SPPNet. Our models were evaluated on a KTH dataset and 켐1 dataset separately. The experimental results showed that our model has better performance in the field of video-based behavior recognition in comparison to the existing models. |
| Related Links | https://www.mdpi.com/1999-5903/10/12/115 |
| e-ISSN | 19995903 |
| DOI | 10.3390/fi10120115 |
| Journal | Future Internet |
| Issue Number | 12 |
| Volume Number | 10 |
| Language | English |
| Publisher | MDPI AG |
| Publisher Date | 2018-01-01 |
| Publisher Place | Switzerland |
| Access Restriction | Open |
| Subject Keyword | Information technology Cnn Action Recognition Spatial Pyramid Pooling Dense Connectivity 3d Convolution |
| Content Type | Text |
| Resource Type | Article |
