Multiple Model Approach to Deformable Shape Tracking

This paper describes a new proposal for tracking deformable objects in video sequences using multiple shape models of heterogeneous dimensionality. This models are generated unsupervisedly from a training sequence, and used to estimate the shape of an object along time by means of a novel tracking framework proposed. This framework is based in estimate the rigid and non-rigid shape transformations in two separated but related processes. The advantage of proceed in that way is that the a priori knowledge contained in the learned models is better exploited, resulting in a more reliable tracking performance. The Condensation algorithm is used to estimate the rigid transformation of the shape, while the non-rigid shape deformation is determined by combining the response of several Kalman Filters. The proposal is evaluated tracking a synthetic form, and the silhouette of a pedestrian.