Pedestrian detection in far-infrared daytime images using a hierarchical codebook of SURF

One of the main challenges in intelligent vehicles concerns pedestrian detection for driving assistance. Recent experiments have showed that state-of-the-art descriptors provide better performances on the far-infrared (FIR) spectrum than on the visible one, even in daytime conditions, for pedestrian classification. In this paper, we propose a pedestrian detector with on-board FIR camera. Our main contribution is the exploitation of the specific characteristics of FIR images to design a fast, scale-invariant and robust pedestrian detector. Our system consists of three modules, each based on speeded-up robust feature (SURF) matching. The first module allows generating regions-of-interest (ROI), since in FIR images of the pedestrian shapes may vary in large scales, but heads appear usually as light regions. ROI are detected with a high recall rate with the hierarchical codebook of SURF features located in head regions. The second module consists of pedestrian full-body classification by using SVM. This module allows one to enhance the precision with low computational cost. In the third module, we combine the mean shift algorithm with inter-frame scale-invariant SURF feature tracking to enhance the robustness of our system. The experimental evaluation shows that our system outperforms, in the FIR domain, the state-of-the-art Haar-like Adaboost-cascade, histogram of oriented gradients (HOG)/linear SVM (linSVM) and MultiFtrpedestrian detectors, trained on the FIR images

Fusion de données dans un système multicapteurs pour l'étude du mouvement d'un objet dans une scène réelle

Dans ce papier, nous présentons une nouvelle méthode de détermination des paramètres de rotation d'un objet sans connaissance a priori. Cette méthode exploite le fait que l'état d'une onde réfléchie est fonction de l'orientation de la surface observée. Une mise en correspondance spatio-temporelle fondée sur l'étude des histogrammes procure la valeur de l'angle de rotation autour d'un axe quelconque. Des résultats expérimentaux sur scènes réelles sont présentés

Kernel on bag of paths for measuring similarity of shapes

Abstract. A common approach for classifying shock graphs is to use a dissimilarity measure on graphs and a distance based classifier. In this paper, we propose the use of kernel functions for data mining problems on shock graphs. The first contribution of the paper is to extend the class of graph kernel by proposing kernels based on bag of paths. Then, we propose a methodology for using these kernels for shock graphs retrieval. Our experimental results show that our approach is very competitive compared to graph matching approaches and is rather robust.

Object categorization using kernels combining graphs and histogram of gradients

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Pedestrian detection using infrared images and histograms of oriented gradients

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. I

Pedestrian Detection using Infrared images and

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

An Ensemble Learning Method Based on Random Subspace Sampling for Palmprint Identification

Palmprint recognition is an important and widely used biometric modality with high reliability, stability and user acceptability. In this paper we propose a simple and effective ensemble learning method for palmprint identification based on Random Subspace Sampling (RSS). To achieve it, we rely on 2D-PCA to build the random subspaces. As 2D-PCA is an unsurpevised technique, features are extracted in each subspace using 2D-LDA. A simple 1-Nearest Neighbor classifier is associated to each subspace, the final decision rule being obtained by majority voting rule. The experimental results on multispectral and PolyU palmprint datasets show very encouraging performances compared to state-of-the-art techniques.This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 7-1711-1-312. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University

A Cooperative Approach to Vision-Based Vehicle Detection

In this paper two different vision based systems for vehicle detection are described and their integration discussed. The first approach is based on the use of a specific model for vehicles and mostly relies on monocular vision. Conversely, the second system is based on the use of stereo vision and allows to refine the coarse results obtained by the former. A preliminary integration of the two systems has been tested on the ARGO experimental vehicle and some remarks about reliability and robustness are also included

Stereo Vision-based Feature Extraction for Vehicle Detection

This paper presents a stereo vision system for vehicle detection. It has been conceived as the integration of two different subsystems. Initially a stereo vision based system is used to recover the most relevant 3D features in the scene; due to the algorithm's generality, all the vertical features are extracted as potentially belonging to a vehicle in front of the vision system. This list of significant patterns is fed to a second subsystem based on monocular vision; it processes the list computing a match with a general model of a vehicle based on symmetry and shape, thus allowing the identification of the sole characteristics belonging to a vehicle