Name
Abstract | ||
|---|---|---|
Background modeling plays an important role for video surveillance, object tracking, and object counting. In this paper, we propose a novel deep background modeling approach utilizing fully convolutional network. In the network block constructing the deep background model, three atrous convolution branches with different dilate are used to extract spatial information from different neighborhoods o... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TITS.2017.2754099 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Convolution,Computational modeling,Feature extraction,Adaptation models,Biological neural networks,Object detection,Mathematical model | Spatial analysis,Computer vision,Object detection,Convolution,Feature extraction,Video tracking,Pixel,Artificial intelligence,Engineering,Computation | Journal |
Volume | Issue | ISSN |
19 | 1 | 1524-9050 |
Citations | PageRank | References |
4 | 0.37 | 51 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Yang | 1 | 5 | 2.07 |
| Jing Li | 2 | 28 | 3.68 |
| Yuansheng Luo | 3 | 4 | 0.71 |
| Yang Zhao | 4 | 254 | 11.07 |
| Hong Cheng | 5 | 703 | 65.27 |
| Jun Li | 6 | 22 | 5.71 |