Person tracking based on a hybrid neural probabilistic model

Wenjie Yan , Cornelius Weber , Stefan Wermter

Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011) pages 365--372, doi: 10.1007/978-3-642-21738-8_47 - Jun 2011

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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.

@InProceedings{YWW11a, 
 	 author =  {Yan, Wenjie and Weber, Cornelius and Wermter, Stefan},  
 	 title = {Person tracking based on a hybrid neural probabilistic model}, 
 	 booktitle = {Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN 2011)},
 	 number = {},
 	 volume = {},
 	 pages = {365--372},
 	 year = {2011},
 	 month = {Jun},
 	 publisher = {Springer},
 	 doi = {10.1007/978-3-642-21738-8_47}, 
 }