Name
Abstract | ||
|---|---|---|
Pedestrian detection in the wild remains a challenging problem especially for scenes containing serious occlusion. In this paper, we propose a novel feature learning method in the deep learning framework, referred to as Feature Calibration Network (FC-Net), to adaptively detect pedestrians under various occlusions. FC-Net is based on the observation that the visible parts of pedestrians are select... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TITS.2020.3041679 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | DocType | Volume |
Feature extraction,Calibration,Detectors,Deep learning,Visualization,Training,Object detection | Journal | 23 |
Issue | ISSN | Citations |
5 | 1524-9050 | 0 |
PageRank | References | Authors |
0.34 | 35 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianliang Zhang | 1 | 4 | 2.41 |
| Jie Chen | 2 | 392 | 65.58 |
| Baochang Zhang | 3 | 1130 | 93.76 |
| Jianzhuang Liu | 4 | 1614 | 98.72 |
| Xiaopeng Zhang | 5 | 301 | 17.50 |
| Qi Tian | 6 | 6443 | 331.75 |