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.

• Type: Journal
• Year: 2011
• Where: ICANN
• Authors: Wenjie Yan, Cornelius Weber, Stefan Wermter
• Keywords: ICANN 2011, Neural Networks, Short Term Memory, Sigma Pi, Time Person