A single target voting scheme for traffic sign detection

Houben, Sebastian

doi:10.1109/ivs.2011.5940429

Cited by 63 publications

(38 citation statements)

References 10 publications

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“…Using the Canny edge detection or other variations, the edges can be detected [78], [79], [80], [81]. The histogram of oriented gradients (HOG) method was also proposed.…”

Section: B Shape Featuresmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

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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“…Using the Canny edge detection or other variations, the edges can be detected [78], [79], [80], [81]. The histogram of oriented gradients (HOG) method was also proposed.…”

Section: B Shape Featuresmentioning

confidence: 99%

Overview of Environment Perception for Intelligent Vehicles

Zhu

Yuen

Mihaylova

et al. 2017

IEEE Trans. Intell. Transport. Syst.

294

125

View full text Add to dashboard Cite

show abstract

“…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: 99%

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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“…To achieve a reliable detection performance under different lighting situations, traffic sign detection works have been conducted based on Y'CBCR [16], HSV [17], and CIECAM97 [18] color models. Except for global features, local invariant features, such as points of interest/regions [19], MSER [20], and Hough-like feature [21], have also been used in traffic sign detection. Moreover, Gonzalez-Jorge et al [22] applied a 3-D laser scanner for traffic sign detection and reflectance inspection regarding the highly reflective radiation on traffic signs.…”

Section: A Studies On Traffic Sign Detection and Recognitionmentioning

confidence: 99%

Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

Wen

Luo

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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This paper presents a spatial-related traffic sign inspection process for sign type, position, and placement using mobile laser scanning (MLS) data acquired by a RIEGL VMX-450 system and presents its potential for traffic sign inventory applications. First, the paper describes an algorithm for traffic sign detection in complicated road scenes based on the retroreflectivity properties of traffic signs in MLS point clouds. Then, a point cloud-to-image registration process is proposed to project the traffic sign point clouds onto a 2-D image plane. Third, based on the extracted traffic sign points, we propose a traffic sign position and placement inspection process by creating geospatial relations between the traffic signs and road environment. For further inventory applications, we acquire several spatial-related inventory measurements. Finally, a traffic sign recognition process is conducted to assign sign type. With the acquired sign type, position, and placement data, a spatial-associated sign network is built. Experimental results indicate satisfactory performance of the proposed detection, recognition, position, and placement inspection algorithms. The experimental results also prove the potential of MLS data for automatic traffic sign inventory applications.

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A single target voting scheme for traffic sign detection

Cited by 63 publications

References 10 publications

Overview of Environment Perception for Intelligent Vehicles

Overview of Environment Perception for Intelligent Vehicles

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Spatial-Related Traffic Sign Inspection for Inventory Purposes Using Mobile Laser Scanning Data

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