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Optical Engineering • Open Access

Importance of detection for video surveillance applications
Author(s): Javier Varona; Jordi Gonzalez; Ignasi Rius; Juan Jose Villanueva

Paper Abstract

Though it is the first step of a real video surveillance application, detection has received less attention than tracking in research on video surveillance. We show, however, that the majority of errors in the tracking task are due to wrong detection. We show this by experimenting with a multi object tracking algorithm based on a Bayesian framework and a particle filter. This algorithm, which we have named iTrack, is specifically designed to work in practical applications by defining a statistical model of the object appearance to build a robust likelihood function. Likewise, we present an extension of a background subtraction algorithm to deal with active cameras. This algorithm is used in the detection task to initialize the tracker by means of a prior density. By defining appropriate performance metrics, the overall system is evaluated to elucidate the importance of detection for video surveillance applications.

Paper Details

Date Published: 1 August 2008
PDF: 9 pages
Opt. Eng. 47(8) 087201 doi: 10.1117/1.2965548
Published in: Optical Engineering Volume 47, Issue 8
Show Author Affiliations
Javier Varona, Univ. de les Illes Balears (Spain)
Jordi Gonzalez, UPC-CSIC (Spain)
Ignasi Rius, Univ. Autònoma de Barcelona (Italy)
Juan Jose Villanueva, Univ. Autònoma de Barcelona (Spain)

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