Fusion multi-sources pour l'interprétation d'un environnement routier

Exceeding speed limits is a major cause of road accidents, which could be reduced by the use of robust detection of speed limits that may continuously inform the driver of the proper speed limitation. The work presented in this document relate to the achievement of such a system based on a visual detection of speed limit signs. To make the system robust, it is necessary to merge the results of these detections with information from other sensors to interpret the results of the visual detection. For this aim, two algorithms were developed. First, a specific geographic information system was developed in order to expand the electronic horizon of the vehicle. The fusion process in place addressing these various sources of information is based on model-based rules to overcome the problems inherent to the probabilistic fusion process that can sometimes lead to uncertain situations putting the whole system in global fault. These works are the fruit of collaboration with an automotive supplier and the prototype has been validated experimentally on the road and in real conditions. A ground truth tool has been specially developed to quantify the results. The system shows excellent results with high detection and classification rates for speed limit signs recognition and complex situations analysis.Le dépassement des limitations de vitesse est l'une des causes majeures des accidents de la route, qui pourraient être réduits par l'utilisation de système robuste de détection des limitations de vitesse pouvant continuellement informer le conducteur de la bonne limitation imposée. Les travaux présentés dans ce document portent sur la réalisation d'un tel système basé sur une détection visuelle des panneaux de limitation de vitesse. Afin de rendre le système robuste, il est nécessaire de fusionner les résultats de ces détections avec les informations d'autres capteurs pour interpréter les résultats issus de la détection visuelle. C'est ainsi qu'a été entre autre spécialement développé un capteur cartographique permettant d'avoir une vision plus large sur l'horizon électronique du véhicule, ainsi qu'un système détection des lignes de marquage au sol pour analyser les changements de voie. Le processus de fusion mis en place traitant ces diverses sources d'information est fondé sur des modèles à base de règles permettant de s'affranchir des problèmes inhérents aux processus de fusion probabilistes pouvant parfois mener à des situations de doute mettant le système global en faute. Ces travaux sont le fruit d'une collaboration avec un industriel et le prototype développé a été validé expérimentalement sur route. Un outil de vérité terrain a été spécialement développé pour quantifier les résultats. Le système montre d'excellents résultats en détection et reconnaissance des panneaux de limitation de vitesse ainsi que dans la clarification de situations complexes

Robust on-vehicle real-time visual detection of American and European speed limit signs, with a modular Traffic Signs Recognition system

International audienceIn this paper, we present robust visual speed limit signs detection and recognition systems for American and European signs. Both are variants of the same modular traffic signs recognition architecture, with a sign detection step based only on shape-detection (rectangles or circles), which makes our systems insensitive to color variability and quite robust to illumination variations. Instead of a global recognition, our system classifies (or rejects) the speed-limit sign candidates by segmenting potential digits inside them, and then applying a neural network digit recognition. This helps handling global sign variability, as long as digits are properly recognized. The global sign detection rate is around 90% for both (standard) U.S. and E.U. speed limit signs, with a misclassification rate below 1%, and not a single validated false alarm in >150 minutes of recorded videos. The system processes in real-time videos with images of 640x480 pixels, at ~20frames/s on a standard 2.13GHz dual-core laptop

Improving pan-European speed-limit signs recognition with a new “global number segmentation” before digit recognition

International audienceIn this paper, we present an improved European speed-limit sign recognition system based on an original “global number segmentation” (inside detected circles) before digit segmentation and recognition. The global speed-limit sign detection and correct recognition rate, currently evaluated on videos recorded on a mix of French and German roads, is around 94 %, with a misclassification rate below 1%, and not a single validated false alarm in several hours of recorded videos. Our greyscale-based system is intrinsically insensitive to colour variability and quite robust to illumination variations, as shown by an on-road evaluation under bad weather conditions (cloudy and rainy) which yielded 84% good detection and recognition rate, and by a first night-time on-road evaluation with 75% correct detection rate. Due to recognition occurring at digit level, our system has the potential to be very easily extended to handle properly all variants of speed-limit signs from various European countries. Regarding computation load, videos with images of 640x480 pixels can be processed in real-time at ~20frames/s on a standard 2.13GHz dual-core laptop

Modular Traffic Sign Recognition applied to on-vehicle real-time visual detection of American and European speed limit signs

International audienceWe present a new modular traffic signs recognition system, successfully applied to both American and European speed limit signs. Our sign detection step is based only on shape-detection (rectangles or circles). This enables it to work on grayscale images, contrary to most European competitors, which eases robustness to illumination conditions (notably night operation). Speed sign candidates are classified (or rejected) by segmenting potential digits inside them (which is rather original and has several advantages), and then applying a neural digit recognition. The global detection rate is ~90% for both (standard) U.S. and E.U. speed signs, with a misclassification rate 150 minutes of video. The system processes in real-time ~20 frames/s on a standard high-end laptop

Joint interpretation of on-board vision and static GPS cartography for determination of correct speed limit

We present here a first prototype of a "Speed Limit Support" Advance Driving Assistance System (ADAS) producing permanent reliable information on the current speed limit applicable to the vehicle. Such a module can be used either for information of the driver, or could even serve for automatic setting of the maximum speed of a smart Adaptive Cruise Control (ACC). Our system is based on a joint interpretation of cartographic information (for static reference information) with on-board vision, used for traffic sign detection and recognition (including supplementary sub-signs) and visual road lines localization (for detection of lane changes). The visual traffic sign detection part is quite robust (90% global correct detection and recognition for main speed signs, and 80% for exit-lane sub-signs detection). Our approach for joint interpretation with cartography is original, and logic-based rather than probability-based, which allows correct behaviour even in cases, which do happen, when both vision and cartography may provide the same erroneous information

Detection and recognition of end-of-speed-limit and supplementary signs for improved european speed limit support

International audienceWe present two new features for our prototype of European Speed Limit Support system: detection and recognition of end-of-speed-limit signs, as well as a framework for detection and recognition of supplementary signs located below main signs and modifying their scope (particular lane, class of vehicle, etc...). The end-of-speed-limit signs are globallyrecognized by a Multi-Layer Perceptron (MLP) neural network. The supplementary signs are detected by applying a rectangle-detection in a region below recognized speed-limit signs, followed by a MLP neural network recognition. A common French+German end-of-speed-limit signs recognition has been designed and successfully tested, yielding 82% detection+recognition. Results for detection and recognition of a first kind of supplementary sign (French exit-lane) are already satisfactory (78% correct detection rate), and our framework can easily be extended to handle other types of supplementary signs. To our knowledge, we are the first team presenting results on detection and recognition of supplementary signs below speed signs, which is a crucial feature for a reliable Speed Limit Support

Method of circle detection in images for round traffic sign identification and vehicle driving assistance device.

Innovincia reference : BRT0580 Valeo Schalter und Sensoren GmbHThe invention relates to a method of circle detection in images for round traffic sign identification, including two stages: - a first stage of detection (101) to detect centers of a group of candidates circles in an image by: - calculating the edges on said image, - applying a Hough transform on said edges to identify the centers of a group of candidates circles, and - a second stage of validation (102) where a circle is selected from said group of candidates circles, characterized in that in said second stage of validation (102), a dispersion gradient criterion measuring the luminance gradient dispersion is calculated for each candidate circle of said group of candidates circles and in that the candidate circle among said group of candidates circles that has the lowest dispersion gradient criterion is selected as a detected circle to identify a round traffic sign. The invention also relates to a vehicle driving assistance device to implement said method of circle detection in images for round traffic sign identification.L'invention concerne un procédé de détection cercle dans les images pour l'identification des panneaux de signalisation circulaires. Elle comprend deux étapes: - Une première étape de détection: pour détecter les centres d'un groupe de cercles candidats dans une image par: - Le calcul des bords sur ladite image, - L'application d'une transformée de Hough sur lesdits bords afin d'identifier les centres d'un groupe de cercles candidats, et - Une deuxième phase de validation (102) où un cercle est sélectionné dans ce groupe de cercles candidats, caractérisé en ce que ladite seconde étape de validation (102), un critère de dispersion de gradient mesure la dispersion du gradient de luminance est calculé pour chaque cercle candidats dudit groupe de cercles candidats et en ce que le cercle des candidats dudit groupe de cercles candidats qui a le plus bas critère de dispersion gradient est sélectionné comme un cercle détecté pour identifier un panneau de signalisation rond. L'invention concerne également la mise en place d'un dispositif d'assistance à la conduite de véhicules impliquant la mise en oeuvre dudit procédé de détection cercles dans les images pour l'identification des panneaux de circulation ronds

Towards a reliable speed-limit assistant combining Traffic Sign Recognition with GPS information

Being able to always know the current speed-limit is a highly interesting feature both for driver information system, and for advanced driving assistance such as a smart Adaptive Cruise Control (ACC) automatically adapting vehicle speed to current speed-limit. However, this kind of function can be really valuable and of practical use only if the produced speed-limit information is extremely reliable. Using vision-based Traffic Sign Recognition alone cannot be robust enough, as there will always be cases in which some sign is missed because of occlusion by another vehicle. Conversely, the system cannot rely only on cartographic navigation information, because it has to take into account roadwork temporary limits as well as variable speed limits enforced by LED signs, and also supplementary signs located below main signs and modifying their scope (class of vehicle, concerned lane, etc...), which requires quite robust vision-based recognition of several kinds of traffic-signs. Even proprioceptive information such as current speed, turn indicators, steering wheel angle (, etc...) could be necessary for a correct system. In this paper, we present several of the required bricks for building such a reliable speed-limit support system: a performant recognition of speed-limit-signs working on various European roads (despite sign variability between country) and able to operate correctly also on LED signs and at night, good detection of end-of-speed-limit-signs, a method for effective detection and recognition of supplementary signs, and a general framework for intelligent fusion, using Belief Theory, of information deduced from vision and navigation. All these functions are implemented modularly, and have been evaluated both on real recorded videos, and by on-road real-time tests