Name
Abstract | ||
|---|---|---|
Visual recognition of objects through multiple observations is an important component of object tracking. We address the problem of vehicle matching when multiple observations of a vehicle are separated in time such that frames of observations are not contiguous, thus prohibiting the use of standard frame-to-frame data association. We employ features extracted over a sequence during one time interval as a vehicle fingerprint that is used to compute the likelihood that two or more sequence observations are from the same or different vehicles. The challenges of change in pose, aspect and appearances across two disparate observations are handled by combining feature-based quasi-rigid alignment with flexible matching between two or more sequences. The current work uses the domain of vehicle tracking from aerial platforms where typically both the imaging platform and the vehicles are moving and the number of pixels on the object are limited to fairly low resolutions. Extensive evaluation with respect to ground truth is reported in the paper. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/CVPR.2005.364 | computer vision and pattern recognition |
Keywords | Field | DocType |
Vehicle Fingerprinting,object tracking,aerial platform,vehicle tracking,flexible matching,vehicle fingerprint,different vehicle,current work,time interval,multiple observation,sequence observation | Computer vision,Pattern recognition,Fingerprint recognition,Computer science,Feature extraction,Fingerprint,Ground truth,Video tracking,Pixel,Artificial intelligence,Vehicle tracking system,Cognitive neuroscience of visual object recognition | Conference |
ISBN | Citations | PageRank |
0-7695-2372-2 | 16 | 1.39 |
References | Authors | |
8 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanlin Guo | 1 | 240 | 29.25 |
| Steve Hsu | 2 | 172 | 16.40 |
| Ying Shan | 3 | 641 | 45.51 |
| Harpreet Sawhney | 4 | 265 | 14.93 |
| Rakesh Kumar | 5 | 1923 | 157.44 |