Name
Abstract | ||
|---|---|---|
This paper proposes a data fusion scheme for visual object identification and tracking by autonomous vehicles. In this scheme, image motion vectors fields, color features, visual disparity depth information and camera motion parameters are fused together to identify the target 3D visual and dynamic features. This paper also presents a detailed description of the 3D target tracking algorithm using an extended Kalman filter with a constant velocity dynamic model. Performance of the proposed scheme is discussed through experimental results. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1109/ICARCV.2004.1469790 | ICARCV |
Keywords | Field | DocType |
visual information fusion,visual object identification,3d target tracking,kalman filters,target tracking,camera motion,autonomous vehicles,image motion vectors fields,object tracking,cameras,constant velocity dynamic model,color features,visual disparity depth information,remotely operated vehicles,extended kalman filter,data fusion scheme,sensor fusion,robot vision,data fusion,3d visualization | Remotely operated underwater vehicle,Computer vision,Extended Kalman filter,Robot vision,Computer science,Image motion,Sensor fusion,Kalman filter,Artificial intelligence,Visual Disparity,Information fusion | Conference |
Volume | ISSN | ISBN |
3 | 2474-2953 | 0-7803-8653-1 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Jia | 1 | 58 | 5.53 |
| Arjuna P. Balasuriya | 2 | 130 | 11.30 |
| Subhash Challa | 3 | 252 | 24.96 |