Name
Abstract | ||
|---|---|---|
We propose a nonlinear covariance region descriptor for target tracking. The target object appearance and spatial information is represented using a covariance matrix in a target derived Hilbert space using kernel principal component analysis. A similarity measure is derived, which computes the similarity of a candidate image region to the learned covariance matrix. A variational technique is provided to maximize the similarity measure, which iteratively finds the best matched object region. Tracking performance is demonstrated on a variety of sequences containing noise, occlusions, illumination changes, background clutter, etc. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICIP.2009.5414297 | Image Processing |
Keywords | Field | DocType |
Hilbert spaces,covariance matrices,feature extraction,image matching,image sequences,iterative methods,object detection,principal component analysis,target tracking,Hilbert space,background clutter,covariance matrix,kernel covariance image region,kernel principal component analysis,object tracking,spatial information representation,target tracking,tracking performance,visual tracking,Visual tracking | Kernel (linear algebra),Object detection,Computer vision,Similarity measure,Pattern recognition,Computer science,Kernel principal component analysis,Video tracking,Artificial intelligence,Covariance matrix,Principal component analysis,Covariance | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-5655-0 | 978-1-4244-5655-0 | 6 |
PageRank | References | Authors |
0.53 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar Arif | 1 | 22 | 5.87 |
| Patricio Antonio Vela | 2 | 11 | 2.30 |