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Pareto Optimal Sensing Strategies for an Active Vision System

Enrique Dunn 1 Gustavo Olague 1 Evelyne Lutton 2 Marc Schoenauer 3 
3 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : We present a multi-objective methodology, based on evolutionary computation, for solving the sensor planning problem for an active vision system. The application of different representation schemes, that allow to consider either xed or variable size camera networks in a single evolutionary process, is studied. Furthermore, a novel representation of the recombination and mutation operators is brought forth. The developed methodology is incorporated into a 3D simulation environment and experimental results shown. Results validate the exibility and effectiveness of our approach and offer new research alternatives in the eld of sensor planning.
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Submitted on : Monday, November 23, 2020 - 10:31:55 PM
Last modification on : Tuesday, October 25, 2022 - 4:18:31 PM
Long-term archiving on: : Thursday, February 25, 2021 - 1:40:59 PM


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  • HAL Id : inria-00000845, version 1


Enrique Dunn, Gustavo Olague, Evelyne Lutton, Marc Schoenauer. Pareto Optimal Sensing Strategies for an Active Vision System. CEC 2004, Jun 2004, Portland, United States. ⟨inria-00000845⟩



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