Using Fast Classification of Static and Dynamic Environment for Improving Bayesian Occupancy Filter (BOF) and Tracking

Qadeer Baig 1 Mathias Perrollaz 1 Jander Botelho 1 Christian Laugier 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Type de document :
Communication dans un congrès
In IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), Dec 2012, Guangzou, China, China. 2012
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https://hal.inria.fr/hal-00757400
Contributeur : Qadeer Baig <>
Soumis le : lundi 26 novembre 2012 - 17:36:51
Dernière modification le : jeudi 11 octobre 2018 - 08:48:02

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  • HAL Id : hal-00757400, version 1

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INRIA | UGA | LIG | LIG_SRCPR | LIG_SIC_E-MOTION

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Qadeer Baig, Mathias Perrollaz, Jander Botelho, Christian Laugier. Using Fast Classification of Static and Dynamic Environment for Improving Bayesian Occupancy Filter (BOF) and Tracking. In IEEE International Conference on Control, Automation, Robotics and Vision (ICARCV), Dec 2012, Guangzou, China, China. 2012. 〈hal-00757400〉

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