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Tag - pedestrian detection

Fil des billets

mardi, septembre 11 2007

Noyaux multiples : sélection de modèle appliquée à la détection de piétons

21e colloque GRETSI sur le traitement du signal et des images 11-14 septembre 2007, Troyes, France

Abstract This paper presents a pedestrian detection method based on the multiple kernel framework. One main problematic of pattern recognition resides in the pertinent characterization of the data. Depending on the descriptor, we sometimes have to tune the descriptor in order to be more efficient. Instead of accomplishing this tuning manually by testing and comparing all possible values we propose here to use the multiple kernel framework. The aim is to use a kernel as a linear combination of different kernels in order to combine and select automatically the best kernels within a set of kernels. This can be assimilated as model selection, where one kernel of the set corresponds to one model. We first introduce the MKL framework and finally apply this approach for a parameter tuning task and a feature selection problem.

mardi, juin 13 2006

Pedestrian Detection using Infrared images and Histograms of Oriented Gradients

Intelligent Vehicles Symposium 13-15 juin 2006, Tokyo, Japon

Abstract This paper presents a complete method for pedestrian detection applied to infrared images. First, we study an image descriptor based on histograms of oriented gradients (HOG), associated with a Support Vector Machine (SVM) classifier and evaluate its efficiency. After having tuned the HOG descriptor and the classifier, we include this method in a complete system, which deals with stereo infrared images. This approach gives good results for window classification, and a preliminary test applied on a video sequence proves that this approach is very promising.

author = {Frédéric Suard and Alain Rakotomamonjy and Abdelaziz Bensrhair and Alberto Broggi},
title = {Pedestrian Detection using Infrared images and Histograms of Oriented Gradients},
booktitle = {Intelligent Vehicles Symposium, Tokyo, Japan},
year = {2006},
month = {June},
pages = {206--212},}

mardi, septembre 6 2005

Détection de piétons par stéréovision et noyaux de graphes

20e colloque GRETSI sur le traitement du signal et des images 6-9 septembre 2005, Louvain-La-Neuve, Belgique.

Abstract This article presents a novel method concerning pedestrian detection, thanks to graph kernels. Nowadays, the pedestrian detection is a hard task, due to the variability of its shape : size and posture. To address this problem, we choose to transform a pedestrian into a graph. The aim of this method consists of extracting a graph from each object (pedestrian or non-pedestrian), contained in a database. We compute the kernel with the inner product between graphs in order to apply a supervised classifier, here the SVMs (Support Vector Machine). We applied this method on a real images database in order to test its efficiency, particularly for scale invariance, and we obtained a good classification rate.

Résumé – Cet article présente une méthode concernant la reconnaissance de piétons à l’aide de graphes et de méthodes à noyaux. La détection du piéton est limitée à cause de la grande variabilité de la forme du piéton : taille, posture. Pour surmonter ce problème, nous avons choisi de le représenter à l’aide d’un graphe. Le but de la méthode est d’extraire le graphe de chaque objet (piétons ou non-piétons) présent dans une base d’images et de calculer un noyau à partir de ces graphes afin d’effectuer un apprentissage supervisé basé sur les SVMs (Séparateurs à Vaste Marge). L’application sur une base d’images réelles nous permet de démontrer l’efficacité de cette méthode, au niveau des invariances en échelle, avec un bon taux de reconnaissance.

author = {Frédéric Suard and Alain Rakotomamonjy and Abdelaziz Bensrhair},
title = {Détection de piétons par stéréovision et noyaux de graphes},
booktitle = {GRETSI05, Louvain-la-Neuve, Belgique},
year = {2005},
pages = {686-686},}

lundi, juin 6 2005

Pedestrian Detection using Stereo-vision and Graph Kernels

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},}