Name
Abstract | ||
|---|---|---|
In this ariticle, a data fusion based algorithm is proposed to identify and track moving objects for autonomous vehicle navigation.
It is a challenging problem because both the object and the cameras are moving. Here, the optical flow vector field, color
features, and stereo pair disparities are used as visual features, while the vehicle’s motion-sensor data are used to determine
the cameras’ motion. We propose a data fusion algorithm which integrates information obtained from different visual cues and
the vehicle’s motion-sensor data for target-tracking. The fusion algorithm determines the velocity and position of the target
in the 3D world coordinates. Next, we present a detailed description of the three-dimensional (3D) target-tracking algorithm
using an extended Kalman filter. Experimental results are presented to demonstrate the performance of the proposed scheme
using different natural image sequences. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s10015-007-0499-8 | Artificial Life and Robotics |
Keywords | DocType | Volume |
autonomous vehicles · image clustering and segmentation · extended kalman fi lter · motion · optical fl ow · sensor data fusion · stereo · template matching · vision,extended kalman filter,optical flow,vector field,data fusion,visual cues,three dimensional,sensor fusion,template matching | Journal | 12 |
Issue | ISSN | Citations |
1 | 16147456 | 1 |
PageRank | References | Authors |
0.36 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhen Jia | 1 | 58 | 5.53 |
| Arjuna Balasuriya | 2 | 64 | 5.17 |
| Subhash Challa | 3 | 252 | 24.96 |