Name
Abstract | ||
|---|---|---|
This paper presents a preceding vehicle detection and tracking system by using Support Vector Machine- Based Particle filtering (SVMPF). SVMPF integrates the Support Vector Machine (SVM) score with sampling weights. The sample weights, which are used to construct a probability distribution of samples, are measured by the SVM score. Once the vehicle is detected and tracked, it changes to SVM tracking mode which is simpler than the previous SVMPF mode. In the experiments, we demonstrate that our system can track the preceding vehicles under different whether conditions. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICPR.2006.1178 | ICPR (2) |
Keywords | Field | DocType |
vision-based preceding vehicle detection,sampling weight,preceding vehicle detection,support vector machine,previous svmpf mode,preceding vehicle,sample weight,svm score,probability distribution,vehicle tracking,computer vision,statistical distributions,particle filtering,tracking system,particle filter,support vector machines | Computer vision,Object detection,Pattern recognition,Computer science,Support vector machine,Particle filter,Tracking system,Vehicle detection,Probability distribution,Sampling (statistics),Artificial intelligence,Vehicle tracking system | Conference |
ISSN | ISBN | Citations |
1051-4651 | 0-7695-2521-0 | 10 |
PageRank | References | Authors |
1.14 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-Ming Fu | 1 | 384 | 30.00 |
| Chung-Lin Huang | 2 | 540 | 37.61 |
| Yi-Sheng Chen | 3 | 10 | 1.14 |