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Mixed-State CONDENSATION pour suivi et ré-identification simultanés dans des réseaux de caméras à champs de vue disjoints

Abstract : This article presents a novel approach to person tracking within large-scale environments monitored by nonoverlapping field-of-view camera networks. We address the image-based tracking problem with distributed particle filters using a hierarchical color model. The novelty of our approach resides in the embedding of an already-seenpeople database in the particle filter framework. Doing so, the filter performs not only image position estimation but also does establish identity probabilities for the current targets in the network. Thus we use online person re-identification as a way to introduce continuity to track people in disjoint camera networks. No calibration stage is required. We demonstrate the performances of our approach on a network of 5 disjoint cameras and a 16-person database.
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https://hal.inria.fr/inria-00597657
Contributor : Peter Sturm <>
Submitted on : Wednesday, June 1, 2011 - 3:14:19 PM
Last modification on : Friday, June 25, 2021 - 9:48:03 AM
Long-term archiving on: : Friday, November 9, 2012 - 2:10:45 PM

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  • HAL Id : inria-00597657, version 1

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Boris Meden, Patrick Sayd, Frédéric Lerasle. Mixed-State CONDENSATION pour suivi et ré-identification simultanés dans des réseaux de caméras à champs de vue disjoints. ORASIS - Congrès des jeunes chercheurs en vision par ordinateur, INRIA Grenoble Rhône-Alpes, Jun 2011, Praz-sur-Arly, France. ⟨inria-00597657⟩

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