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Proceedings Paper

Optimization of the SSD multiple kernel tracking applied to IR video sequences
Author(s): Brais Martinez; Luis Ferraz; Jose Diaz-Caro; Xavier Binefa
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Paper Abstract

This paper addresses the problem of tracking a target in an IR video sequence using a kernel based histogram representation of the target. In this field, gradient ascent methods have demonstrated useful results with weighted kernels and in particular Mean Shift is currently the most commonly used gradient scale method. Our approximation follows the work made by Hager, that uses a SSD objective function (derived from Matusita metric) and combines it with a Newton-like maximization method, resulting a fast gradient scale system. An important property is that this method enables the use of multiple kernels, allowing a more powerful representation with a minimum increasing of computational cost. We analyse the limitation of this representation using the Newton maximization algorithm and we introduce the concept of direction of ambiguity. This concept allows a criterion for choosing the kernels that drive the iteration to minimize the error criterion. The results we present show the improvements of the method over a tracking problem. The target is a small car with a great background similarity.

Paper Details

Date Published: 11 October 2005
PDF: 10 pages
Proc. SPIE 5987, Electro-Optical and Infrared Systems: Technology and Applications II, 59870C (11 October 2005); doi: 10.1117/12.630746
Show Author Affiliations
Brais Martinez, Univ. Autònoma de Barcelona (Spain)
Luis Ferraz, Univ. Autònoma de Barcelona (Spain)
Jose Diaz-Caro, Ctr. de Investigación y Desarrollo de la Armada (Spain)
Xavier Binefa, Univ. Autònoma de Barcelona (Spain)


Published in SPIE Proceedings Vol. 5987:
Electro-Optical and Infrared Systems: Technology and Applications II
Ronald G. Driggers; David A. Huckridge, Editor(s)

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