Name
Abstract | ||
|---|---|---|
Recently video surveillance techniques have been widely applied to intelligent transportation systems. Tracking of moving objects such as vehicles has become a major topic in video surveillance applications. This paper presents a multi-feature fusion model based on a particle filter for moving object tracking. The particle filter combines color and edge orientation information by a stochastic fusion scheme. The scheme randomly selects single observation model to evaluate the likelihood of some particles. The stochastic selection probability is adjusted adaptively by the uncertainty associated with a feature model. The experiment shows that the proposed method has strong tracking robustness and can effectively solve the occlusion problem. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CIS.2008.86 | CIS (1) |
Keywords | Field | DocType |
video surveillance,strong tracking robustness,object tracking,feature model,single observation model,video surveillance application,multi-feature fusion model,multiple feature fusion,particle filter,video surveillance technique,stochastic selection probability,stochastic fusion scheme,feature extraction,histograms,edge detection,sensor fusion,computational modeling,particle filters,trajectory,intelligent transportation systems,vehicle tracking,probability | Object detection,Computer vision,Pattern recognition,Computer science,Particle filter,Tracking system,Feature extraction,Sensor fusion,Robustness (computer science),Video tracking,Artificial intelligence,Vehicle tracking system | Conference |
Citations | PageRank | References |
3 | 0.47 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huibin Wang | 1 | 29 | 10.99 |
| Chaoying Liu | 2 | 16 | 5.86 |
| Xu Lizhong | 3 | 155 | 24.51 |
| Min Tang | 4 | 11 | 1.78 |
| Xuewen Wu | 5 | 3 | 0.47 |