A hierarchical feature fusion framework for adaptive visual tracking

Authors:
Alexandros Makris;Dimitrios Kosmopoulos;Stavros Perantonis;Sergios Theodoridis
Affiliations:
NCSR Demokritos, Institute for Informatics and Telecommunications, Computational Intelligence Laboratory, 15310, Aghia Paraskevi, Athens, Greece and University of Athens, Department of Informatics ...;NCSR Demokritos, Institute for Informatics and Telecommunications, Computational Intelligence Laboratory, 15310, Aghia Paraskevi, Athens, Greece;NCSR Demokritos, Institute for Informatics and Telecommunications, Computational Intelligence Laboratory, 15310, Aghia Paraskevi, Athens, Greece;University of Athens, Department of Informatics, 15771 Athens, Greece
Venue:
Image and Vision Computing
Year:
2011

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Abstract

A Hierarchical Model Fusion (HMF) framework for object tracking in video sequences is presented. The Bayesian tracking equations are extended to account for multiple object models. With these equations as a basis a particle filter algorithm is developed to efficiently cope with the multi-modal distributions emerging from cluttered scenes. The update of each object model takes place hierarchically so that the lower dimensional object models, which are updated first, guide the search in the parameter space of the subsequent object models to relevant regions thus reducing the computational complexity. A method for object model adaptation is also developed. We apply the proposed framework by fusing salient points, blobs, and edges as features and verify experimentally its effectiveness in challenging conditions.