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

An article published in ICDP 2011.

Authors: M. Souded, L. Giulieri and F. Bremond

The authors address the problematic of the multi-object tracking in video surveillance context with single static cameras. They propose a novel approach for multi-object tracking in a particle filtering and data association framework allowing real-time tracking and dealing with the most important challenges (1) selecting and tracking real object of interest in noisy environments (2) managing situations of occlusion. In this study they consider tracker inputs from motion detection approach (classically based on background subtraction and clustering). Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. The article presents SIFT features tracking into a particle filtering and data association. The performance of the proposed algorithm is evaluated on sequences from ETISEO, CAVIAR, PETS2001 and VS-PETS2003 datasets in order to show the improvements upon state-of-the-art methods.

Diagram of the proposed object tracking framework



Full version of the article can be downloaded at here.

Leave a Reply