Name
Abstract | ||
|---|---|---|
Shadow detection and removal is an important task for on-road visual perception. However, effectively detecting and removing the shadows on the road to maintain illumination consistency remain challenging. Road shadows blur and change road features, greatly damaging the image quality and making further detection and tracking more difficult to implement. For example, detection could be mistaken due to incorrect identification of shadow boundaries. The adaptability and accuracy can be affected as well. To tackle the problems, this paper employs Support Vector Machine (SVM) based on color saliency space and gradient field to detect shadow. Nonlinear SVM classifier analyzes its color saliency space and gradient information, then reconstructs road shadow descriptor to distinguish shadowed regions. To remove the shadow, adaptive variable scale regional compensation operator is adopted. Following experiments verify the detection and removal method are feasible in the real world, and robust to many types of road conditions. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TIV.2020.2987440 | IEEE Transactions on Intelligent Vehicles |
Keywords | DocType | Volume |
Color saliency space,gradient field,shadow detection and removal,regional compensation operator | Journal | 5 |
Issue | ISSN | Citations |
4 | 2379-8858 | 1 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chunxiang Wang | 1 | 13 | 5.73 |
| Hanqing Xu | 2 | 1 | 0.34 |
| Zhiyu Zhou | 3 | 18 | 5.32 |
| liuyuan deng | 4 | 9 | 1.81 |
| Ming Yang | 5 | 91 | 30.46 |