Descripteurs pour la reconnaissance de piétons

National audienceLa reconnaissance de piétons dans les images est une tâche à part entière qui requiert l'utilisation d'outils particuliers. Parmi les descripteurs récents utilisés pour la détection de piétons, on trouve les ondelettes de Haar, les histogrammes d'orientation de gradients et les descripteurs binaires. Ce papier présente une comparaison des performances de ces trois différents descripteurs à partir d'une base d'images commune et d'un même classifieur. Nous présenterons également comment associer ces descripteurs de façon simple pour améliorer les taux de reconnaissance de piétons

Real Time Parallel Implementation of a Particle Filter Based Visual Tracking

8ppWe describe the implementation of a 3D visual tracking al- gorithm on a cluster architecture.Parallelisation of the algorithm makes it possible to obtain real-time execution (more than 20 FPS) even with large state vectors, which has been proven difficult on sequential architecture. Thanks to a user-friendly software development environment, this large gain in performance is not obtained at the price of programmability

Real-Time Tracking with Classifiers

Two basic facts motivate this paper: (1) particle filter based trackers have become increasingly powerful in recent years, and (2) object detectors using statistical learning algorithms often work at a near real-time rate. We present the use of classifiers as likelihood observation function of a particle filter. The original resulting method is able to simultaneously recognize and track an object using only a statistical model learnt from a generic database. Our main contribution is the definition of a likelihood function which is produced directly from the outputs of a classifier. This function is an estimation of calibrated probabilities P (class|data). Parameters of the function are estimated to minimize the negative log likelihood of the training data, which is a cross-entropy error function. Since a generic statistical model is used, the tracking does not need any image based model learnt inline. Moreover, the tracking is robust to appearance variation because the statistical learning is trained with many poses, illumination conditions and instances of the object. We have implemented the method for two recent popular classifiers: (1) Support Vector Machines and (2) Adaboost. An experimental evaluation shows that the approach can be used for popular applications like pedestrian or vehicle detection and tracking. Finally, we demonstrate that an efficient implementation provides a real-time system on which only a fraction of CPU time is required to track at frame rate

Méthode d'apprentissage pour la classification à partir d'exemples positifs

National audienceCet article présente une méthode d'apprentissage générique et non supervisée à partir d'une seule base d'exemple positif, pour la classification. Le système est formalisé dans un cadre probabiliste. Nous proposons une méthode originale pour approximer la densité de probabilité de la fonction de vraisemblance correspondant aux évènements dit normaux, en utilisant un modèle parcimonieux basé sur des fonctions noyaux. Ce modèle présente l'avantage des méthodes non-paramétriques tout en limitant le coût algorithmique souvent important qui leur est lié. La classification est ensuite effectuée à partir de cette approximation grâce à une notion de confiance. La méthode sera comparée à celle des One Class SVM et testée dans le cas de la détection d'événements rares liés au trafic routier

An efficient method to compute the inverse jacobian matrix in visual servoing An efficient method to compute the inverse jacobian matrix in visual servoing

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Boost.simd: generic programming for portable simdization

ABSTRACT SIMD extensions have been a feature of choice for processor manufacturers for a couple of decades. Designed to exploit data parallelism in applications at the instruction level, these extensions still require a high level of expertise or the use of potentially fragile compiler support or vendor-specific libraries. While a large fraction of their theoretical accelerations can be obtained using such tools, exploiting such hardware becomes tedious as soon as application portability across hardware is required. In this paper, we describe Boost.SIMD, a C++ template library that simplifies the exploitation of SIMD hardware within a standard C++ programming model. Boost.SIMD provides a portable way to vectorize computation on Altivec, SSE or AVX while providing a generic way to extend the set of supported functions and hardwares. We introduce a C++ standard compliant interface for the users which increases expressiveness by providing a high-level abstraction to handle SIMD operations, an extension-specific optimization pass and a set of SIMD aware standard compliant algorithms which allow to reuse classical C++ abstractions for SIMD computation. We assess Boost.SIMD performance and applicability by providing an implementation of BLAS and image processing algorithms

RealTime tracking with occlusion end illumination variations

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Triangulation for Points on Lines

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