PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Pedestrian Detection using Stereo-vision and Graph Kernels
Frederic Suard, Vincent Guigue, Alain Rakotomamonjy and Abdelaziz Bensrhair
In: IEEE Symposium on Intelligent Vehicule, Las Vegas(2005).


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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:1388
Deposited By:Alain Rakotomamonjy
Deposited On:28 November 2005