Name
Abstract | ||
|---|---|---|
Intelligent vehicle perception involves the correct detection and tracking of moving objects. Taking into account all the possible information at early levels of the perception task can improve the final model of the environment. In this paper, we present an evidential fusion framework to represent and combine evidence from multiple lists of sensor detections. Our fusion framework considers the position, shape and appearance information to represent, associate and combine sensor detections. Although our approach takes place at detection level, we propose a general architecture to include it as a part of a whole perception solution. Several experiments were conducted using real data from a vehicle demonstrator equipped with three main sensors: lidar, radar and camera. The obtained results show improvements regarding the reduction of false detections and mis-classifications of moving objects. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IVS.2014.6856555 | Dearborn, MI |
Keywords | Field | DocType |
cameras,image fusion,intelligent transportation systems,object detection,object tracking,optical radar,LIDAR,camera,evidential fusion framework,frontal object perception,intelligent vehicle perception,moving objects detection,moving objects tracking,radar,vehicle demonstrator | Radar,Computer vision,Computer science,Image processing,Fusion,Lidar,Artificial intelligence,Perception,Information fusion | Conference |
ISSN | Citations | PageRank |
1931-0587 | 1 | 0.37 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ricardo Omar Chávez García | 1 | 3 | 1.44 |
| Trung-Dung Vu | 2 | 76 | 6.57 |
| Olivier Aycard | 3 | 309 | 26.57 |
| Chavez-Garcia, R.O. | 4 | 1 | 0.37 |