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.
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Yan, W., Weber, C., Wermter, S. (2011). Person Tracking Based on a Hybrid Neural Probabilistic Model. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds) Artificial Neural Networks and Machine Learning – ICANN 2011. ICANN 2011. Lecture Notes in Computer Science, vol 6792. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21738-8_47
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DOI: https://doi.org/10.1007/978-3-642-21738-8_47
Publisher Name: Springer, Berlin, Heidelberg
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