Person tracking based on a hybrid neural probabilistic model

Authors:
Wenjie Yan;Cornelius Weber;Stefan Wermter
Affiliations:
University of Hamburg, Department of Informatics, Knowledge Technology, Hamburg, Germany;University of Hamburg, Department of Informatics, Knowledge Technology, Hamburg, Germany;University of Hamburg, Department of Informatics, Knowledge Technology, Hamburg, Germany
Venue:
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Year:
2011

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Abstract

This article presents a novel approach for a real-time person tracking system based on particle filters that use different visual streams. Due to the difficulty of detecting a person from a top view, a new architecture is presented that integrates different vision streams by means of a Sigma-Pi network. A short-term memory mechanism enhances the tracking robustness. Experimental results show that robust real-time person tracking can be achieved.