Real-Time Speed Sign Detection Using the Radial Symmetry Detector

Barnes, Nick; Zelinsky, Alex; Fletcher, Luke

doi:10.1109/tits.2008.922935

Cited by 154 publications

(84 citation statements)

References 26 publications

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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: 93%

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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Section: A Traffic Sign Detectionmentioning

confidence: 93%

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Ellahyani¹,

Ansari²,

Jaafari³

et al. 2016

ijacsa

View full text Add to dashboard Cite

show abstract

“…The spatial extent is then an arithmetic average of the set of voters, and therefore the scale is not discretized. That is an advantage over methods like [1] using a multi-scale approach (one accumulator per scale).…”

Section: Bilateral Chinese Transform Bctmentioning

confidence: 99%

“…The Radial Symmetry Transform RST was first used for road sign detection in [1]: greyscale images are used but only 60 mph or 40 mph circular speed signs are detected. This transform suffers from several impairments: it can only detect circular shapes, and it yields to a large number of false positives.…”

Section: Introductionmentioning

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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“…This paper presents a TLD algorithm inspired by the approaches followed for road sign detection, by (Barnes and Zelinsky, 2004), (Siogkas and Dermatas, 2006) and (Barnes et al, 2008). The Fast Radial Symmetry (FRS) detector of (Loy and Zelinsky, 2003) is employed in the referenced approaches, to take advantage of the symmetrical geometry of road signs.…”

Section: Introductionmentioning

confidence: 99%

Traffic Lights Detection in Adverse Conditions Using Color, Symmetry and Spatiotemporal Information

Siogkas¹,

Skodras²,

Dermatas³

2012

Proceedings of the International Conference on Computer Vision Theory and Applications

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Abstract:This paper proposes the use of a monocular video camera for traffic lights detection, in a variety of conditions, including adverse weather and illumination. The system incorporates a color pre-processing module to enhance the discrimination of red and green regions in the image and handle the "blooming effect" that is often observed in such scenes. The fast radial symmetry transform is utilized for the detection of traffic light candidates and finally false positive results are minimized using spatiotemporal persistency verification. The system is qualitatively assessed in various conditions, including driving in the rain, at night and in city roads with dense traffic, as well as their synergy. It is also quantitatively assessed on a publicly available manually annotated database, scoring high detection rates.

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Real-Time Speed Sign Detection Using the Radial Symmetry Detector

Cited by 154 publications

References 26 publications

Traffic Sign Detection and Recognition using Features Combination and Random Forests

Traffic Sign Detection and Recognition using Features Combination and Random Forests

A Real-Time Road Sign Detection Using Bilateral Chinese Transform

Traffic Lights Detection in Adverse Conditions Using Color, Symmetry and Spatiotemporal Information

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