Integration of visual and depth information for vehicle detection

Alexandros Makris 1 Mathias Perrollaz 1 Igor Paromtchik 1 Christian Laugier 1
1 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : In this work an object class recognition method is presented. The method uses local image features and follows the part based detection approach. It fuses intensity and depth information in a probabilistic framework. The depth of each local feature is used to weight the probability of finding the object at a given scale. To train the system for an object class only a database of annotated with bounding boxes images is required, thus automatizing the extension of the system to different object classes. We apply our method in the problem of detecting vehicles captured from a moving platform. The experiments in a data-set of stereo images captured in an urban environment show a significant improvement in performance when using both information modalities.
Type de document :
Communication dans un congrès
International Conference on Intelligent Robots and Systems, Workshop on Perception and Navigation for Autonomous Vehicles in Human Environment, 2011, San Francisco, United States. 2011
Liste complète des métadonnées

Littérature citée [27 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-00683755
Contributeur : Mathias Perrollaz <>
Soumis le : jeudi 29 mars 2012 - 18:23:00
Dernière modification le : vendredi 12 octobre 2018 - 01:18:13
Document(s) archivé(s) le : samedi 30 juin 2012 - 02:36:23

Fichier

Makris_IROS11_Workshop_final.p...
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00683755, version 1

Collections

INRIA | LIG | UGA | LIG_SRCPR | LIG_SIC_E-MOTION

Citation

Alexandros Makris, Mathias Perrollaz, Igor Paromtchik, Christian Laugier. Integration of visual and depth information for vehicle detection. International Conference on Intelligent Robots and Systems, Workshop on Perception and Navigation for Autonomous Vehicles in Human Environment, 2011, San Francisco, United States. 2011. 〈hal-00683755〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

251

Téléchargements de fichiers

135

Contact


archive-ouverte@inria.fr