Intelligent Vehicles Symposium 6-8 juin 2005, Las Vegas, Nevada, Etats-Unis.

Abstract This paper presents a method for pedestrian detection with stereovision and graph comparison. Images are segmented thanks to the NCut method applied on a single image, and the disparity is computed from a pair of images. This segmentation enables us to keep only shapes of potential obstacles, by eliminating the background. The comparison between two graphs is accomplished with a inner product for graph, and then the recognition stage is performed learning is done among several pedestrian and non-pedestrian graphs with SVM method. The results that are depicted are preliminary results but they show that this approach is very promising since it clearly demonstrates that our graph representation is able to deal with the variability of pedestrian pose.

author = {Frédéric Suard and Vincent Guigue and Alain Rakotomamonjy and Abdelaziz Bensrhair},
 title = {Pedestrian Detection using Stereo-vision and Graph Kernels},
 booktitle = {Intelligent Vehicles Symposium, Las Vegas, Nevada},
 year = {2005},
 month = {June},
 pages = {267--272},}