On-line learning of shape information for object segmentation and tracking

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 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.