Name
Abstract | ||
|---|---|---|
•This paper proposes a new end-to-end pedestrian detection method called the super-resolution detection (SRD) network that aims to solve the low-quality and occlusion problems in intelligent video surveillance.•To verify the effectiveness of the proposed SRD algorithm, a new low-quality playground (PG) dataset for pedestrian detection is collected that provides dense and occluded pedestrians with light interference and motion blur in the surveillance images.•Compared with the state-of-the-art methods, our proposed SRD method achieves higher accuracy of pedestrian detection based on the PG dataset. In particular, we demonstrate improved results for more difficult detection cases (light interference and occluded), and overall higher localization precision. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2021.107846 | Pattern Recognition |
Keywords | DocType | Volume |
Pedestrian detection,Low-quality,SRGAN,Faster R-CNN | Journal | 115 |
Issue | ISSN | Citations |
1 | 0031-3203 | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Jin | 1 | 122 | 29.25 |
| Yue Zhang | 2 | 184 | 53.93 |
| Yigang Cen | 3 | 116 | 20.90 |
| Yidong Li | 4 | 7 | 5.24 |
| Vladimir Mladenovic | 5 | 6 | 2.81 |
| v v voronin | 6 | 24 | 11.19 |