Name
Abstract | ||
|---|---|---|
This paper aims at generating high-quality object proposals for object detection in autonomous driving. Most existing proposal generation methods are designed for the general object detection, which may not perform well in a particular scene. We propose several geometrical features suited for autonomous driving and integrate them into state-of-the-art general proposal generation methods. In particular, we formulate the integration as a feature fusion problem by fusing the geometrical features with existing proposal generation methods in a Bayesian framework. Experiments on the challenging KITTI benchmark demonstrate that our approach improves the existing methods significantly. Combined with a convolutional neural net detector, our approach achieves state-of-the-art performance on all three KITTI object classes. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1155/2017/3175186 | MOBILE INFORMATION SYSTEMS |
Field | DocType | Volume |
Data mining,Object detection,Computer vision,Feature fusion,Computer science,Artificial intelligence,Artificial neural network,Detector,Bayesian probability | Journal | 2017 |
ISSN | Citations | PageRank |
1574-017X | 0 | 0.34 |
References | Authors | |
3 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiliu Feng | 1 | 6 | 3.13 |
| Wanzeng Cai | 2 | 0 | 0.34 |
| Xiaolong Liu | 3 | 0 | 0.34 |
| Huini Fu | 4 | 0 | 0.34 |
| Yafei Liu | 5 | 0 | 0.68 |
| Hengzhu Liu | 6 | 86 | 23.28 |