Online subjective feature selection for occlusion management in tracking applications

Authors:
Loris Bazzani;Marco Cristani;Manuele Bicego;Vittorio Murino
Affiliations:
Dipartimento di Informatica, University of Verona, Verona, Italy;Dipartimento di Informatica, University of Verona, Verona, Italy;Dipartimento di Informatica, University of Verona, Verona, Italy;Dipartimento di Informatica, University of Verona, Verona, Italy
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Most of the state-of-the-art tracking algorithms are prone to error when dealing with occlusions, especially when the involved moving objects are hardly discernible in appearance. In this paper, we propose a multi-object particle filtering tracking framework particularly suited to manage the occlusion problem. The presented solution consists in the introduction of a online subjective feature selection mechanism, which highlights and employs the most discriminant features characterizing a single object with respect to the neighbouring objects. The policy adopted fits formally in the observation step of the particle filtering process, it is effective and not computationally costly. Trials carried out on illustrative synthetic data and on recent challenging benchmark sequences report compelling performances and encourage further development of the technique.