Traffic sign shape classification and localization based on the normalized FFT of the signature of blobs and 2D homographies

Jiménez, Pedro Gil; Bascón, Saturnino Maldonado; Moreno, Hilario Gómez; Arroyo, Sergio Lafuente; Ferreras, Francisco López

doi:10.1016/j.sigpro.2008.06.019

Cited by 63 publications

(21 citation statements)

References 7 publications

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“…A Haar wavelet-like feature for traffic sign is another alternative [68], [84]. Furthermore, some other methods were proposed, such as distance to bounding box [85], fast Fourier transform (FFT) of shape signatures [86], tangent functions [71], simple image patches [70], and combinations of various simple features [77]. In [64], integral channel features and aggregate channel features were proposed to detect U.S. traffic signs.…”

Section: B Shape Featuresmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

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Abstract-This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area.Index Terms-Intelligent vehicles, environment perception and modeling, lane and road detection, traffic sign recognition, vehicle tracking and behavior analysis, scene understanding.

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Section: B Shape Featuresmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

297

125

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show abstract

“…Distance to bounding box (DtB) is used in [2] as a measure of distances from the contour of a sign candidate to its bounding box. Similarly, the FFT of shape signatures used in [21] is based on the distance from the shape center to its contour at different angles. Tangent functions, which are used in [22], calculate the angles of the tangents at various points around the contour.…”

Section: Road Sign Recognitionmentioning

confidence: 99%

Exploiting Visual Saliency and Bag-of-Words for Road Sign Recognition

Wei

Tang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a novel method for road sign detection and recognition in complex scene real world images. Our algorithm consists of four basic steps. First, we employ a regional contrast based bottom-up visual saliency method to highlight the traffic sign regions, which usually have dominant color contrast against the background. Second, each type of traffic sign has special color distribution, which can be explored by top-down visual saliency to enhance the detection precision and to classify traffic signs into different categories. A bag-of-words (BoW) model and a color name descriptor are employed to compute the specialclass distribution. Third, the candidate road sign blobs are extracted from the final saliency map, which are generated by combining the bottom-up and the top-down saliency maps. Last, the color and shape cues are fused in the BoW model to express blobs, and a support vector machine is employed to recognize road signs. Experiments on real world images show a high success rate and a low false hit rate and demonstrate that the proposed framework is applicable to prohibition, warning and obligation signs. Additionally, our method can be applied to achromatic signs without extra processing.

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“…The goal of such systems is to make driving safer and more reliable [1]. Such systems can benefit from road signs information by applying artificial intelligence and computer vision techniques.…”

Section: Introductionmentioning

confidence: 99%

“…In [7,8], distance to border (DtB) vector is used to build the shape feature vector which is used in shape classification by SVM, while principal component analysis (PCA) and k-nearest neighbor (KNN) classifier are used to detect the sign in [4]. Hough transform and radial symmetry are used to recognize triangular and circular shape road signs in [9].…”

Section: Introductionmentioning

confidence: 99%

Speed Sign Recognition using Shape-based Features

Abukhait¹,

Zyout²,

Mansour³

2013

IJCA

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An efficient shape-based recognition system of U.S. speed limit road signs is presented in this paper. The proposed system accomplishes speed sign detection and recognition processes using three main stages, namely, geometrical-based detection of rectangular road signs, shape-based segmentation and feature extraction, and pattern classification using a K-nearest neighbor classifier (KNN). Twenty shape descriptors are computed for the most discriminative numerals of each detected sign. The proposed system is invariant to scale, rotation, and partial occlusion. The proposed system has been tested in different conditions, including sunny, cloudy, and rainy weather, and the experimental results on 195 speed signs reveals the efficiency of the proposed shape pattern segmentation and feature extraction methods.

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Traffic sign shape classification and localization based on the normalized FFT of the signature of blobs and 2D homographies

Cited by 63 publications

References 7 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Exploiting Visual Saliency and Bag-of-Words for Road Sign Recognition

Speed Sign Recognition using Shape-based Features

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