Détection des panneaux de signalisation routière par accumulation bivariée

Abstract

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....

Triangular traffic signs detection based on RSLD algorithm

“…The detection rate may even be improved with the introduction of colour information. Secondly, the authors of [3] propose to evaluate the robustness of the VBT algorithm [4] in the presence of Gaussian noise. This evaluation has been presented in Grigorescu's database of traffic sign [15] which contains 48 traffic scene images with 40 triangular signs.…”

“…This evaluation has been presented in Grigorescu's database of traffic sign [15] which contains 48 traffic scene images with 40 triangular signs. The experimental conditions described in [4] have been reproduced. The VBT algorithm and the proposed method are compared by adding different levels of Gaussian noise to images.…”

Traffic Saturation Detection using Hough Transform and VANET

“…Let S = { P 1 , P 2 , ..., P n } be a set of n pixels. We define the Preimage of S as : preimage(S) = p i ∈S Dual(p i ) In addition, Belaroussi et al [20] proposed a geometric method using gradient orientation for vertical signage detection in still images, regardless of their position and orientation. This method is fast and does not distinguish between circles and polygons with 4 or more sides.…”

Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture

“…The first stage is useful to reduce the search space and isolate the Region of Interest (ROI), although the detection block [1] selects those blobs that have an appropriate traffic sign shape and the recognition stage identifies the category of the pictogram already detected. Many research focused on improving the recognition stage, although the main one is to improve the detection block because the quality of the detection is necessary to get a good recognition [2].…”