Name
Abstract | ||
|---|---|---|
Long term tracking is a challenging task for many applications. In this paper, we propose a novel tracking approach that can adapt various appearance changes such as illumination, motion, and occlusions, and owns the ability of robust reacquisition after drifting. We utilize a condensation-based method with an online support vector machine as a reliable observation model to realize adaptive tracking. To redetect the target when drifting, a cascade detector based on random ferns is proposed. It can detect the target robustly in real time. After redetection, we also come up with a new refinement strategy to improve the tracker's performance by removing the support vectors corresponding to possible wrong updates by a matching template. Extensive comparison experiments on typical and challenging benchmark dataset illustrate a robust and encouraging performance of the proposed approach. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/TCYB.2014.2301720 | IEEE T. Cybernetics |
Keywords | Field | DocType |
VISUAL TRACKING,MULTIPLE | Computer vision,Pattern recognition,Computer science,Tracking system,Video tracking,Artificial intelligence,Detector,Machine learning | Journal |
Volume | Issue | ISSN |
44 | 11 | 2168-2275 |
Citations | PageRank | References |
4 | 0.38 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyu Yang | 1 | 4 | 1.06 |
| Baopu Li | 2 | 348 | 30.88 |
| Max Q.-H. Meng | 3 | 1477 | 202.72 |