Integration of visual and depth information for vehicle detection - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2011

Integration of visual and depth information for vehicle detection


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.
Fichier principal
Vignette du fichier
Makris_IROS11_Workshop_final.pdf (571.21 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-00683755 , version 1 (29-03-2012)


  • HAL Id : hal-00683755 , version 1


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. ⟨hal-00683755⟩
127 View
138 Download


Gmail Mastodon Facebook X LinkedIn More