Abstract
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. © Springer-Verlag Berlin Heidelberg 2003.
| Original language | English |
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| Pages (from-to) | 782-792 |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 2652 |
| Publication status | Published - 1 Dec 2003 |