Multiple object tracking via prediction and filtering with a sobolev-type metric on curves

Authors:
Eleonora Bardelli;Maria Colombo;Andrea Mennucci;Anthony Yezzi
Affiliations:
Scuola Normale Superiore, Pisa, Italy;Scuola Normale Superiore, Pisa, Italy;Scuola Normale Superiore, Pisa, Italy;School of Electrical Engineering, Georgia Institute of Technology, Atlanta
Venue:
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
Year:
2012

Citing 12
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Abstract

The problem of multi-target tracking of deforming objects in video sequences arises in many situations in image processing and computer vision. Many algorithms based on finite dimensional particle filters have been proposed. Recently, particle filters for infinite dimensional Shape Spaces have been proposed although predictions are restricted to a low dimensional subspace. We try to extend this approach using predictions in the whole shape space based on a Sobolev-type metric for curves which allows unrestricted infinite dimensional deformations. For the measurement model, we utilize contours which locally minimize a segmentation energy function and focus on the multiple contour tracking framework when there are many local minima of the segmentation energy to be detected. The method detects figures moving without the need of initialization and without the need for prior shape knowledge of the objects tracked.