A complete system to determine the speed limit by fusing a GIS and a camera

International audienceDetermining the speed limit on road is a complex task based on the Highway Code and the detection of temporary speed limits. In our system, these two aspects are managed by a GIS (Geographical Information System) and a camera respectively. The vision-based system aims at detecting the roadsigns as well as the subsigns and the lane markings to filter those applicable. The two sources of information are finally fused by using the Belief Theory to select the correct speed limit. The performance of a navigation-based system is increased by 19%

Recognition of Supplementary Signs for Correct Interpretation of Traffic Signs

International audienceTraffic Sign Recognition (TSR) is now relatively well-handled by several approaches. However, traffic signs are often completed by one (or several) supplementary placed below. They are essential for correct interpretation of main sign, as they specify its applicability scope. The main difficulty of supplementary sub-sign recognition is the potentially infinite number of classes, as nearly any information potentially infinite number of classes, as nearly any information can be written on them. In this paper, we propose and evaluate a hierarchical approach for recognition of supplementary signs, in which the "meta-class" of the sub-sign (Arrow, Pictogram, Text or Mixed) is first determined. The classification is based on the pyramid-HOG feature, completed by dark area proportion measured on the same pyramid. Evaluation on a large database of images with and without supplementary signs shows that the classification accuracy of our approach 95% precision and recall. When used on output of our sub-sign specific detection algorithm, the global correct detection and recognition rate is 91%

Subsign detection with region-growing from contrasted seeds

International audienceSpeed limit determination systems for cars based on vision are more and more developed. Roadsign detection is nowadays a well managed problem. However, in some situations this information is not sufficient to know the speed limitation. Restrictions are sometimes applicable and specified by subsigns. These small rectangles often provide essential information about the applicability scope (vehicle type, condition, lane, etc.) of speed limits. We present an approach of subsign localization based on region growing with an initial step of seed selection using morphological reconstruction. A comparison is also performed with three other techniques based on edge, color and graph on two databases gathering French and German subsigns. The obtained subsign correct detection is above 65%

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

Speed limit determination by real-time embedded visual and cartographical data fusion

Les systèmes d'aide à la conduite sont de plus en plus présents dans nos véhicules et nous garantissent un meilleur confort et plus de sécurité. Dans cette thèse, nous nous sommes particulièrement intéressés aux systèmes d'adaptation automatique de la vitesse limite. Nous avons proposé une approche alliant vision et navigation pour gérer de façon optimale l'environnement routier.Panneaux, panonceaux et marquages sont autant d'informations visuelles utiles au conducteur pour connaître les limitations temporaires en vigueur sur la route. La reconnaissance des premiers ont fait l'objet ces dernières années d'un grand nombre d'études et sont même commercialisés, contrairement aux seconds. Nous avons donc proposé un module de détection et classification de panonceaux sur des images à niveaux de gris. Un algorithme de reconstruction morphologique associé à une croissance de régions nous ont permis de concentrer la segmentation sur les zones fortement contrastées de l'image entourées d'un ensemble de pixels d'intensité similaire. Les rectangles ainsi détectés ont ensuite fait l'objet d'une classification au moyen de descripteurs globaux de type PHOG et d'une structure hiérarchique de SVMs. Afin d'éliminer en dernier lieu les panonceaux ne s'appliquant pas à la voie sur laquelle circule le véhicule, nous avons pris en compte les informations de marquages à l'aide d'une machine d'états.Après avoir élaboré un module de vision intégrant au mieux toutes les informations disponibles, nous avons amélioré le système de navigation. Son objectif est d'extraire d'une base de données embarquée, le contexte de conduite lié à la position du véhicule. Ville ou non, classe fonctionnelle et type de la route, vitesse limite sont extraits et modélisés sous forme d'attributs. La fiabilité du capteur est ensuite calculée en fonction du nombre de satellites visibles et de la qualité de numérisation du réseau. La confiance en chaque vitesse limite sera alors fonction de ces deux ensembles.La fusion des deux sources au moyen de Demspter-Shafer a conduit à de très bonnes performances sur nos bases de données et démontré l'intérêt de tous ces éléments.ADAS (Autonomous Driving Assistance Systems) are more and more integrated in vehicles and provide to drivers more confort and safety. In this thesis, we focused on Intelligent Speed Adaptation. We proposed an approach combining vision and navigation in order to optimally manage the driving context information.Roadsigns, subsigns and markings are visual data used by the driver to determine the current temporary speed limitations. Many research were conducted during last years to recognise the first one, contrary to the second. Commercialised products are even implemented in vehicles. We thus developped a subsign detection and classification module using greyscale images. A morphological reconstruction with a growing region helped us to focus the segmentation on highly contrasted pixels surrounded by homogeneous regions. Global descriptors such as PHOGs combined to a hierarchical structure of SVMs were then used to classify the output rectangles. Finally, we eliminated subsigns which are not applicable to the current lane by considering markings.After having developed a vision module integrating all the available information, we improved the navigation system. The objective was to extract from an embedded database the driving context related to the vehicle position. Urban context or not, functional class, road type and speed limit were collected and modelised into criteria. The sensor reliability was then computed and depended on the satellite configuration and the network digitisation quality. Confidence in each speed limit combined all these elements.Fusion of both sources with the Dempster-Shafer theory led to very good performances on our databases et showed the importance of all the used information

Détermination de la vitesse limite par fusion de données vision et cartographiques temps-réel embarquées

ADAS (Autonomous Driving Assistance Systems) are more and more integrated in vehicles and provide to drivers more confort and safety. In this thesis, we focused on Intelligent Speed Adaptation. We proposed an approach combining vision and navigation in order to optimally manage the driving context information.Roadsigns, subsigns and markings are visual data used by the driver to determine the current temporary speed limitations. Many research were conducted during last years to recognise the first one, contrary to the second. Commercialised products are even implemented in vehicles. We thus developped a subsign detection and classification module using greyscale images. A morphological reconstruction with a growing region helped us to focus the segmentation on highly contrasted pixels surrounded by homogeneous regions. Global descriptors such as PHOGs combined to a hierarchical structure of SVMs were then used to classify the output rectangles. Finally, we eliminated subsigns which are not applicable to the current lane by considering markings.After having developed a vision module integrating all the available information, we improved the navigation system. The objective was to extract from an embedded database the driving context related to the vehicle position. Urban context or not, functional class, road type and speed limit were collected and modelised into criteria. The sensor reliability was then computed and depended on the satellite configuration and the network digitisation quality. Confidence in each speed limit combined all these elements.Fusion of both sources with the Dempster-Shafer theory led to very good performances on our databases et showed the importance of all the used information.Les systèmes d'aide à la conduite sont de plus en plus présents dans nos véhicules et nous garantissent un meilleur confort et plus de sécurité. Dans cette thèse, nous nous sommes particulièrement intéressés aux systèmes d'adaptation automatique de la vitesse limite. Nous avons proposé une approche alliant vision et navigation pour gérer de façon optimale l'environnement routier.Panneaux, panonceaux et marquages sont autant d'informations visuelles utiles au conducteur pour connaître les limitations temporaires en vigueur sur la route. La reconnaissance des premiers ont fait l'objet ces dernières années d'un grand nombre d'études et sont même commercialisés, contrairement aux seconds. Nous avons donc proposé un module de détection et classification de panonceaux sur des images à niveaux de gris. Un algorithme de reconstruction morphologique associé à une croissance de régions nous ont permis de concentrer la segmentation sur les zones fortement contrastées de l'image entourées d'un ensemble de pixels d'intensité similaire. Les rectangles ainsi détectés ont ensuite fait l'objet d'une classification au moyen de descripteurs globaux de type PHOG et d'une structure hiérarchique de SVMs. Afin d'éliminer en dernier lieu les panonceaux ne s'appliquant pas à la voie sur laquelle circule le véhicule, nous avons pris en compte les informations de marquages à l'aide d'une machine d'états.Après avoir élaboré un module de vision intégrant au mieux toutes les informations disponibles, nous avons amélioré le système de navigation. Son objectif est d'extraire d'une base de données embarquée, le contexte de conduite lié à la position du véhicule. Ville ou non, classe fonctionnelle et type de la route, vitesse limite sont extraits et modélisés sous forme d'attributs. La fiabilité du capteur est ensuite calculée en fonction du nombre de satellites visibles et de la qualité de numérisation du réseau. La confiance en chaque vitesse limite sera alors fonction de ces deux ensembles.La fusion des deux sources au moyen de Demspter-Shafer a conduit à de très bonnes performances sur nos bases de données et démontré l'intérêt de tous ces éléments

Improvement of multisensor fusion in speed limit determination by quantifying navigation reliability

International audienceSpeed limit determination is a complex task that may be solved by fusing data from GIS (Geographical Information System) and camera sensor. Among the existing data fusion models the Dempster-Shafer Belief Theory is found to be the most appropriate in this application. A confidence measure weights each source output, namely speed limit present on road sign and driving situation. Using the discounting scheme of Dempster-Shafer, we propose a new way of computing the navigation confidence measure by taking into account the reliability of the GIS. Preliminary tests showed that our method achieves promising results and solves conflicts between vision and navigation-based system