An Object Tracking in Particle Filtering and Data Association Framework, Using SIFT FeaturesReport as inadecuate




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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.





Author: Malik Souded - Laurent Giulieri - Francois Bremond -

Source: https://hal.archives-ouvertes.fr/



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