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Dynamic object detection fusing LIDAR data and images

descriptionPublicationkeyboard_double_arrow_right Conference object 01 Jan 2014 Spain
Authors: Ortega Jimenez, Agustin Alberto; Andrade-Cetto, Juan;

handle: 10261/127584 , 2117/28040

Dynamic object detection fusing LIDAR data and images

Abstract

We present a method to segment dynamic objects on point clouds using images and 3D laser data. Per-pixel background classes are adapted online as Gaussian Mixtures independently for each sensor. The learned classes are fused labeling pixels/voxels that belong to either the background, or the dynamic objects We pay special attention in the calibration and synchronization modules to reach accuracy in registration and data association. We show results of people segmentation in indoor scenes using a Velodyne sensor at a high frame-rate.

This work acknowledges support from a PhD scholarship from the Mexican Council of Science and Technology (CONACYT) and a Research Stay Grant from the Agencia de Gestio d’Ajuts Universitaris i de Recerca (AGAUR) of The Generalitat of Catalonia (2012 CTP00013) for A. Ortega as well as the Spanish Ministry of Economy and Competitiveness Project PAU+ (DPI-2011-27510).

Trabajo presentado al X Taller-Escuela de Procesamiento de Imágenes (PI14) celebrado en Guanajuato (México) del 15 al 16 de octubre del 2014.

Peer Reviewed

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Spain
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Keywords

Classificació INSPEC::Pattern recognition::Computer vision, :Informàtica::Automàtica i control [Àrees temàtiques de la UPC], Àrees temàtiques de la UPC::Informàtica::Automàtica i control, :Pattern recognition::Computer vision [Classificació INSPEC], computer vision

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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