Name
Abstract | ||
|---|---|---|
Visual tracking has been an active area of research in computer vision. However, robust tracking is still a challenging task due to cluttered backgrounds, occlusions and pose variations in the real world. To improve the tracking robustness, this paper proposes a tracking method based on multi-cue adaptive fusion. In this method, multiple cues, such as color and shape, are fused to represent the target observation. When fusing multiple cues, fuzzy logic is adopted to dynamically adjust each cue weight in the observation according to its associated reliability in the past frame. In searching and tracking object, neural network algorithm is applied, which improves the searching efficiency. Experimental results show that the proposed method is robust to illumination changes, pose variations, partial occlusions, cluttered backgrounds and camera motion. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72395-0_116 | ISNN (3) |
Keywords | Field | DocType |
computer vision,visual tracking,fuzzy logic,neural network | Computer vision,Pattern recognition,Computer science,Fuzzy logic,Fusion,Tracking system,Robustness (computer science),Eye tracking,Artificial intelligence,Artificial neural network,Machine learning | Conference |
Volume | Issue | ISSN |
4493 LNCS | PART 3 | 0302-9743 |
Citations | PageRank | References |
3 | 0.43 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong-Wei Li | 1 | 116 | 15.27 |
| Shiqiang Hu | 2 | 56 | 6.96 |
| Peng Guo | 3 | 7 | 1.25 |