We propose a dynamical model-based approach for tracking the shape and deformation of highly deforming objects from time-varying imagery. Previous works have assumed that the object deformation is smooth, which is realistic for the tracking problem, but most have restricted the deformation to belong to a finite-dimensional group, such as affine motions, or to finitely-parameterized models. This, however, limits the accuracy of the tracking scheme. We exploit the smoothness assumption implicit in previous work, but we lift the restriction to finite-dimensional motions/deformations. To do so, we derive analytical tools to define a dynamical model on the (infinite-dimensional) space of curves. To demonstrate the application of these ideas to object tracking, we construct a simple dynamical model on shapes, which is a first-order approximation to any dynamical system. We then derive an associated nonlinear filter that estimates and predicts the shape and deformation of a object from image measurements.
|Titolo:||Tracking deforming objects by filtering and prediction in the space of curves|
|Titolo del libro:||Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on|
|Data di pubblicazione:||2009|
|Nome del convegno:||The 48th IEEE Conference on Decision and Control, and 28th Chinese Control Conference. CDC/CCC 2009.|
|Parole Chiave:||shape recognition; optical tracking; curve fitting; nonlinear filters; object detection; image motion analysis|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1109/CDC.2009.5400786|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|