Name
Abstract | ||
|---|---|---|
We describe a particle filtering method for vision based tracking of a hand held calibrated camera in real-time. The ability of the particle filter to deal with non-linearities and non-Gaussian statistics suggests the potential to pro- vide improved robustness over existing approaches, such as those based on the Kalman filter. In our approach, the particle filter provides recursive ap- proximations to the posterior density for the 3-D motion parameters. The measurements are inlier/outlier counts of likely correspondence matches for a set of salient points in the scene. The algorithm is simple to implement and we present results illustrating good tracking performance using a 'live' cam- era. We also demonstrate the potential robustness of the method, including the ability to recover from loss of track and to deal with severe occlusion. |
Year | Venue | Keywords |
|---|---|---|
2005 | BMVC | particle filter,real time,kalman filter |
Field | DocType | Citations |
Computer vision,Alpha beta filter,Extended Kalman filter,Capacitor-input filter,Fast Kalman filter,Computer science,Particle filter,Kalman filter,Artificial intelligence,Simultaneous localization and mapping,Auxiliary particle filter | Conference | 69 |
PageRank | References | Authors |
6.64 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark Pupilli | 1 | 212 | 17.39 |
| Andrew Calway | 2 | 645 | 54.66 |