Abstract
We present segmentation and tracking of deformable objects
using non-linear on-line learning of high-level shape information in
the form of a level set function.
The emphasis is for successful tracking of objects that undergo
smooth arbitrary deformations, but without the {\it a priori}
learning of shape constraints. The high-level shape information is
learnt on-line by defining a memory of object samples in a
high-dimensional shape space. These shape samples are then used as
weights via a locally defined shape space kernel function to define
a template against which potential future shapes of the tracked
object can be compared. Results for the successful tracking of a
range of deformable motions are presented.
| Translated title of the contribution | On-line Learning of Shape Information for Object Segmentation and Tracking |
|---|---|
| Original language | English |
| Title of host publication | Proceedings of the 20th British Machine Vision Conference |
| Publisher | BMVA Press |
| Publication status | Published - 2009 |
Bibliographical note
Other page information: -Conference Proceedings/Title of Journal: Proceedings of the 20th British Machine Vision Conference
Other identifier: 2001035