Name
Abstract | ||
|---|---|---|
Tracking free form objects by fitting curve models to their boundaries in real-time is not feasible due to the computational burden of fitting algorithms. In this paper, we propose to do fitting only for certain frames in an image sequence and fill in the missing ones using Kalman filtering technique. An algorithm is presented to track a free-form shaped object, moving along an unknown trajectory, within the camera's field of view (FOV). A discrete steady-state Kalman filter is used to estimate the future positions and orientation of the target object. Kalman filter uses the “related points” extracted from the decomposition of implicit polynomials of target's boundary curves and measured position of target's centroid. Related points undergo the same motion with the curve, hence could be used to estimate the orientation of the target. The resulting algorithm is verified with simulations. |
Year | Venue | Keywords |
|---|---|---|
2005 | Antalya | Kalman filters,curve fitting,image sequences,object tracking,Kalman filtering technique,camera,curve fitting models,discrete steady-state Kalman filter,field of view,fitting algorithms,free form object tracking,free-form planar curve tracking,image sequence |
Field | DocType | ISBN |
Computer vision,Extended Kalman filter,Alpha beta filter,Polynomial,Fast Kalman filter,Kalman filter,Artificial intelligence,Invariant extended Kalman filter,Trajectory,Centroid,Mathematics | Conference | 978-160-4238-21-1 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Burak Yöndem | 1 | 1 | 0.68 |
| Mustafa Ünel | 2 | 154 | 20.71 |
| Aytül Erçil | 3 | 112 | 11.11 |