Real Time Road Signs Recognition

Broggi, Alberto; Cerri, Pietro; Medici, Paolo; Porta, Pier Paolo; Ghisio, G.

doi:10.1109/ivs.2007.4290244

Cited by 134 publications

(63 citation statements)

References 10 publications

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“…In many systems, RGB color segmentation is used for Figure 2.1: RGB color space [1] the task of traffic sign detection. For example, authors in [10] tested YUV and RGB color spaces and finally took the second one because of its lower number of false positives and lower computational time. Moreover, Benallal and Meunier [11] have studied the behavior of the RGB components of many traffic signs and have found this method reliable for road signs segmentation.…”

Section: Rgb Color Spacementioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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Section: Rgb Color Spacementioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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“…Much previous work has addressed the problem of detecting and classifying objects pertinent to autonomous driving such as obstacles on and alongside roads (see, for example, [5]). There also exists literature on the detection of road signs (see, for example, [6], [7]). However, the exploitation of prior maps of large-scale road networks for autonomous driving is a relatively recent development.…”

Section: Related Workmentioning

confidence: 99%

Can priors be trusted? Learning to anticipate roadworks

Mathibela

Osborne

Posner

et al. 2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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Abstract-This paper addresses the question of how much a previously obtained map of a road environment should be trusted for vehicle localisation, during autonomous driving, by assessing the probability that roadworks are being traversed. We compare two formulations of a roadwork prior: one based on Gaussian Process (GP) Classification and the other a more conventional Hidden Markov Model (HMM) in order to model correlations between nearby parts of a vehicle trajectory. Importantly, our formulation allows this prior to be updated efficiently and repeatedly to gain an ever more accurate model of the environment over time. In the absence of, or in addition to, any in-situ observations, information from dedicated web resources can readily be incorporated into the framework. We evaluate our model using real data from an autonomous car and show that although the GP and HMM are roughly commensurate in terms of mapping roadworks, the GP provides a more powerful representation and lower prediction error. Our method allows us to truly map and anticipate roadworks on urban roads.

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“…Alternativement, le canal rouge r = R/(R + G + B) de l'espace rgb normalisé (Dutilleux et al, 2007) ou les rapports R/B et G/B peuvent être préférés (Soheilian et al, 2010) (de la Escalera et al, 1997 . Dans (Broggi et al, 2007) le choix s'est porté sur l'espace YUV après une correction chromatique préalable compte tenu des valeurs des pixels de la chaussée théoriquement gris (R=G=B). D'autres espaces tels que Lab (Reina et al, 2006) ou un mélange d'espaces (Timofte et al, 2009) sont utilisés mais le plus souvent la détermi-nation du seuil est empirique.…”

Section: Les Méthodes De Détection De Panneauxunclassified

Détection des panneaux de signalisation routière par accumulation bivariée

Belaroussi¹,

Tarel²

2010

Traitement du signal

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RÉSUMÉ. Nous présentons une méthode géométrique utilisant l'orientation du gradient pour la détection de la signalisation verticale dans des images fixes, indépendamment de leur position et de leur orientation. La détection est réalisée par une transformation de type accumulateur de Hough bivariée, fondée sur l'utilisation de paires de points avec des contraintes sur leurs gradients. Les panneaux circulaires et polygonaux (non triangulaires) sont détectés par la transformation chinoise bilatérale TCB. Cette transformation est rapide et ne fait pas de distinction entre les cercles et les polygones 4 côtés ou plus. Le cas des panneaux triangulaires est traité par la transformation en sommet et bissectrice TSB, capable de détecter précisemment les bissectrices et les sommets d'un triangle. Les performances de la TCB et de la TSB sont estimées sur plusieurs bases d'images de scènes urbaines.ABSTRACT. We present a real-time approach for circular and polygonal road signs detection in still images, regardless of their pose and orientation. Object detection is performed using a Hough like transform based on accumulation on pairs of points with their gradients. Circular and polygonal (non triangular) traffic signs are detected by the so-called Bilateral Chinese Transform BCT. Triangular traffic signs are processed by the Vertex Bisector Transform VBT. These two approaches are tested on several databases of urban scene.MOTS-CLÉS : transformation en symétrie généralisée, transformation en symétrie pour les coins, transformation en sommet et bissectrice, transformation chinoise, contours orientés, panneaux routiers, temps réel, transformée de Hough. Extended abstractRoad sign identification in images is an important issue, especially for vehicle safety and road management applications. It is usually tackled in three stages : detection, recognition and tracking, and evaluated as a whole. To progress towards better algorithms, we focus in this article on the first stage of the process, namely road sign detection. We focus our work on a feature-based approach to build geometrical models of various kind of shapes : triangle on the one hand, square, diamond, octagon and circle on the other hand.We introduce two cumulative bivariate transformations :-the Bilateral Chinese Transform BCT, tailored for the detection of circular and polygonal road signs with four or more sides.-The Vertex and Bisector Transformation VBT, dedicated to the detection of triangular traffic signs.In the BCT approach (Belaroussi et al., 2009b), object detection is done using a pairwise gradient-based symmetry transform able to detect circles and polygons indistinctly. The BCT seeks the symmetry of shape in the gradient orientation : it decomposes an object into a set of parallel contours with opposite gradients, and models this gradient field symmetry using an accumulation of radial symmetry evidences. Figure 7 illustrates the signed accumulators obtained with the BCT over two elliptical shapes : it is worth noticing that the BCT is also orientation free....

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Real Time Road Signs Recognition

Cited by 134 publications

References 10 publications

Detection and recognition of traffic signs inside the attentional visual field of drivers

Detection and recognition of traffic signs inside the attentional visual field of drivers

Can priors be trusted? Learning to anticipate roadworks

Détection des panneaux de signalisation routière par accumulation bivariée

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