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ICANN
2011
Springer

Person Tracking Based on a Hybrid Neural Probabilistic Model

12 years 7 months ago
Person Tracking Based on a Hybrid Neural Probabilistic Model
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.
Wenjie Yan, Cornelius Weber, Stefan Wermter
Added 29 Aug 2011
Updated 29 Aug 2011
Type Journal
Year 2011
Where ICANN
Authors Wenjie Yan, Cornelius Weber, Stefan Wermter
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