Vers un système de détection et caractérisation par caméra de conditions météo critiques pour la sécurité routière

La présence d'une distance de visibilité réduite sur un réseau routier (épais brouillard, pluie forte, etc.) affecte la sécurité de celui-ci. Nous avons conçu un système de bord de voies qui vise à détecter des situations critiques telles que le brouillard dense ou les fortes chutes de pluie à l'aide d'une caméra vidéo. Les différents traitements d'image sont présentés, en particulier l'estimation de la distance de visibilité, la détection de brouillard, ainsi que la détection de pluie. En se fondant sur les principes sous-jacents de ces algorithmes, une caméra est ensuite spécifiée pour répondre aux besoins exprimés par la norme NF P 99-320 sur la météorologie routière. Des résultats expérimentaux sont présentés ainsi que des perspectives de validation à plus grande échelle.The presence ofa reduced visibility distance on a road network (thick fog, heavy rain, etc.) affects its safety. We designed a roadside system on which aims to detect critical situations such as dense fog or heavy rain with a simple CCTV camera. Different image processing are presented, particularly the estimation of visibility distance, the detection of fog, and the detection of rain. Based on the principles underlying these algorithms, a camera is specified to meet the needs expressed by the standard NF P 99-320 on highway meteorology. Experimental results are presented as well as prospective validation at a bigger scale

Azimuthal anisotropy of charged jet production in root s(NN)=2.76 TeV Pb-Pb collisions

We present measurements of the azimuthal dependence of charged jet production in central and semi-central root s(NN) = 2.76 TeV Pb-Pb collisions with respect to the second harmonic event plane, quantified as nu(ch)(2) (jet). Jet finding is performed employing the anti-k(T) algorithm with a resolution parameter R = 0.2 using charged tracks from the ALICE tracking system. The contribution of the azimuthal anisotropy of the underlying event is taken into account event-by-event. The remaining (statistical) region-to-region fluctuations are removed on an ensemble basis by unfolding the jet spectra for different event plane orientations independently. Significant non-zero nu(ch)(2) (jet) is observed in semi-central collisions (30-50% centrality) for 20 <p(T)(ch) (jet) <90 GeV/c. The azimuthal dependence of the charged jet production is similar to the dependence observed for jets comprising both charged and neutral fragments, and compatible with measurements of the nu(2) of single charged particles at high p(T). Good agreement between the data and predictions from JEWEL, an event generator simulating parton shower evolution in the presence of a dense QCD medium, is found in semi-central collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Forward-central two-particle correlations in p-Pb collisions at root s(NN)=5.02 TeV

Two-particle angular correlations between trigger particles in the forward pseudorapidity range (2.5 2GeV/c. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B. V.Peer reviewe

Event-shape engineering for inclusive spectra and elliptic flow in Pb-Pb collisions at root(NN)-N-S=2.76 TeV

Peer reviewe

Elliptic flow of muons from heavy-flavour hadron decays at forward rapidity in Pb-Pb collisions at root s(NN)=2.76TeV

The elliptic flow, v(2), of muons from heavy-flavour hadron decays at forward rapidity (2.5 <y <4) is measured in Pb-Pb collisions at root s(NN)= 2.76TeVwith the ALICE detector at the LHC. The scalar product, two- and four-particle Q cumulants and Lee-Yang zeros methods are used. The dependence of the v(2) of muons from heavy-flavour hadron decays on the collision centrality, in the range 0-40%, and on transverse momentum, p(T), is studied in the interval 3 <p(T)<10 GeV/c. A positive v(2) is observed with the scalar product and two-particle Q cumulants in semi-central collisions (10-20% and 20-40% centrality classes) for the p(T) interval from 3 to about 5GeV/c with a significance larger than 3 sigma, based on the combination of statistical and systematic uncertainties. The v(2) magnitude tends to decrease towards more central collisions and with increasing pT. It becomes compatible with zero in the interval 6 <p(T)<10 GeV/c. The results are compared to models describing the interaction of heavy quarks and open heavy-flavour hadrons with the high-density medium formed in high-energy heavy-ion collisions. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V.Peer reviewe

Pseudorapidity and transverse-momentum distributions of charged particles in proton-proton collisions at root s=13 TeV

The pseudorapidity (eta) and transverse-momentum (p(T)) distributions of charged particles produced in proton-proton collisions are measured at the centre-of-mass energy root s = 13 TeV. The pseudorapidity distribution in vertical bar eta vertical bar <1.8 is reported for inelastic events and for events with at least one charged particle in vertical bar eta vertical bar <1. The pseudorapidity density of charged particles produced in the pseudorapidity region vertical bar eta vertical bar <0.5 is 5.31 +/- 0.18 and 6.46 +/- 0.19 for the two event classes, respectively. The transverse-momentum distribution of charged particles is measured in the range 0.15 <p(T) <20 GeV/c and vertical bar eta vertical bar <0.8 for events with at least one charged particle in vertical bar eta vertical bar <1. The evolution of the transverse momentum spectra of charged particles is also investigated as a function of event multiplicity. The results are compared with calculations from PYTHIA and EPOS Monte Carlo generators. (C) 2015 CERN for the benefit of the ALICE Collaboration. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Peer reviewe

Détection de conditions de visibilité réduite par caméra bord de voies

Dans le cadre du projet Européen SAFESPOT, nous avons conçu un système bord de voies qui vise à détecter des situations critiques telles que le brouillard dense ou les fortes chutes de pluie à l'aide d'une simple caméra de vidéosurveillance. Ce système est installé dans l'unité bord de voies spécifiée par SAFESPOT et dont l'architecture fonctionnelle est rappelée. Les différents traitements d'image sont présentés, en particulier la détection de brouillard, l'estimation de la distance de visibilité ainsi que la détection de pluie. En se fondant sur les principes théoriques de ces algorithmes, une caméra est ensuite spécifiée pour répondre aux besoins exprimés par la norme NF P 99-320 sur la météorologie routière en matière de visibilité. Des résultats expérimentaux sont présentés ainsi que des perspectives de validation à plus grande échelle

Meteorological Conditions Processing for Vision-based Traffic Monitoring

To monitor their networks, road operators equip them with cameras. Degraded meteorological conditions alter the transportation system operation by modifying the behavior of drivers and by reducing the operation range of the sensors. A vision-based traffic monitoring system is proposed to take fog and rain into account and react accordingly. A background modeling approach, based on a mixture of gaussians, is used to separate the foreground from the background. Since fog is steady weather, the background image is used to detect and quantify it and to restore the images. Since rain is a dynamic phenomenon, the foreground is used to detect it and rain streaks are removed from the images accordingly. The different detection algorithms are described and illustrated using actual images to show their potential benefits. The algorithms may be implemented in existing video-based traffic monitoring systems and allow the multiplication of applications running on roadside cameras

D.: Meteorological conditions processing for vision-based traffic monitoring

To monitor their networks, road operators equip them with cameras. Degraded meteorological conditions alter the transport system operation by modifying the behavior of drivers and by reducing the operation range of the sensors. A vision-based traffic monitoring system is proposed to take fog and rain into account and react accordingly. A background modeling approach, based on a mixture of gaussians, is used to separate the foreground from the background. Since fog is a steady weather, the background image is used to detect, to quantify it and to restore the images. Since rain is a dynamic phenomenon, the foreground is used to detect it and rain streaks are removed from it accordingly. The different detection algorithms are described and illustrated using actual images to foresee their potential benefits. The algorithms may be implemented in existing video-based traffic monitoring systems and allow the multiplication of applications running on roadside cameras.