Angle vertex and bisector geometric model for triangular road sign detection

Belaroussi, Rachid; Tarel, Jean-Philippe

doi:10.1109/wacv.2009.5403030

Cited by 51 publications

(30 citation statements)

References 9 publications

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“…Indeed, the triangle center location is quite fuzzy using this approach. To cope with this issue, we developed a specific geometrical transformation for this case [13].…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Road Sign Detection Using Bilateral Chinese Transform

Belaroussi

Tarel

2009

Advances in Visual Computing

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Abstract. We present a real-time approach for circular and polygonal road signs detection 1 in still images, regardless of their pose and orientation. Object detection is done using a pairwise gradient-based symmetry transform able to detect circles and polygons indistinctly. This symmetry transform of gradient orientation, the so-called Bilateral Chinese Transform BCT, 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. On a test database of 89 images 640x480 containing 92 road signs, 79 are correctly detected (86%) with 25 false positives using the BCT approach in about 30 ms/image.

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“…Indeed, the triangle center location is quite fuzzy using this approach. To cope with this issue, we developed a specific geometrical transformation for this case [13].…”

Section: Discussionmentioning

confidence: 99%

A Real-Time Road Sign Detection Using Bilateral Chinese Transform

Belaroussi

Tarel

2009

Advances in Visual Computing

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“…The first sub-category comprises the works by Bahlmann et al [9] and by Brkic et al [10], whereas in the second we find the Regular Polygon Detector [11], the Radial Symmetry Detector [12], the Vertex Bisector Transform [13], the Bilateral Chinese Transform and, alike, the two schemes of Single Target Voting for triangles and circles proposed by Houben [14]. Many recent approaches use gradient orientation information in the detection phase, for example, in [11], Edge Orientation Histograms are computed over shape-specific subregions of the image.…”

Section: A Traffic Sign Detectionmentioning

confidence: 95%

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Ellahyani¹,

Ansari²,

Jaafari³

et al. 2016

ijacsa

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Abstract-In this paper, we present a computer vision based system for fast robust Traffic Sign Detection and Recognition (TSDR), consisting of three steps. The first step consists on image enhancement and thresholding using the three components of the Hue Saturation and Value (HSV) space. Then we refer to distance to border feature and Random Forests classifier to detect circular, triangular and rectangular shapes on the segmented images. The last step consists on identifying the information included in the detected traffic signs. We compare four features descriptors which include Histogram of Oriented Gradients (HOG), Gabor, Local Binary Pattern (LBP), and Local Self-Similarity (LSS). We also compare their different combinations. For the classifiers we have carried out a comparison between Random Forests and Support Vector Machines (SVMs). The best results are given by the combination HOG with LSS together with the Random Forest classifier. The proposed method has been tested on the Swedish Traffic Signs Data set and gives satisfactory results.

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“…T. Ueta and Y. Sumi and N. Yabuki and S Matsumae [10] used a selforganizing map (SOM) to extract a contour line and recognize the traffic-symbol shape from it.R. Belaroussi and J. Tarel [23] proposed a geometric model of the image gradient orientation to detect triangular symbols. R. Marmo and L. Lombardi [21] used optical flow analysis to identify the rectangular symbols and then by searching gray-level discontinuity on the image and Hough transform for detection of Milepost symbols.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Traffic Sign Recognition System based on Colour Image Segmentation

Patnaik¹,

Patil²

2013

IJCA

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The traffic symbol system has been studies for many years. The system mainly has two phases, 1) Sign detection, 2) Recognition. It is one of the most important research area for enabling vehicle driving assistances. The automatic driving system should be simple in order to detect symbol with high responses. The challenge gets more difficult in order to make system simple while avoiding complex image processing techniques to detect symbol. The propose system consist of three main phases, 1) Frame selection, 2) Symbol detection, 3) Symbol Recognition. In frame selection phase the best frame possibly consisting of traffic symbol is selected from number of frames. In detection phase color image segmentation is perform on given frame in order detect the symbol. In recognition phase the detected traffic symbol is recognized using joint tranceform correlation (JTC) technique which is generally use for pattern matching.

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Angle vertex and bisector geometric model for triangular road sign detection

Cited by 51 publications

References 9 publications

A Real-Time Road Sign Detection Using Bilateral Chinese Transform

A Real-Time Road Sign Detection Using Bilateral Chinese Transform

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Real-Time Traffic Sign Recognition System based on Colour Image Segmentation

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