An Object Tracking in Particle Filtering and Data Association Framework, Using SIFT Features

Malik Souded 1, 2 Laurent Giulieri 2 Francois Bremond 1
1 PULSAR - Perception Understanding Learning Systems for Activity Recognition
CRISAM - Inria Sophia Antipolis - Méditerranée
2 Digital Barriers [France]
Abstract : In this paper, we propose a novel approach for multi-object tracking for video surveillance with a single static camera using particle filtering and data association. The proposed method allows for real-time tracking and deals with the most important challenges: 1) selecting and tracking real objects of interest in noisy environments and 2) managing occlusion. We will consider tracker inputs from classic motion detection (based on background subtraction and clustering). Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. This article presents SIFT feature tracking in a particle filtering and data association framework. The performance of the proposed algorithm is evaluated on sequences from ETISEO, CAVIAR, ETS2001 and VS-PETS2003 datasets in order to show the improvements relative to the current state-of-the-art.
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Communication dans un congrès
International Conference on Imaging for Crime Detection and Prevention (ICDP), Nov 2011, London, United Kingdom. 2011
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Soumis le : jeudi 15 décembre 2011 - 11:19:15
Dernière modification le : mardi 24 juillet 2018 - 15:48:01
Document(s) archivé(s) le : vendredi 16 mars 2012 - 02:20:37

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Malik Souded, Laurent Giulieri, Francois Bremond. An Object Tracking in Particle Filtering and Data Association Framework, Using SIFT Features. International Conference on Imaging for Crime Detection and Prevention (ICDP), Nov 2011, London, United Kingdom. 2011. 〈hal-00647256〉

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