Road sign detection and recognition

Shneier, Michael O.

doi:10.1117/12.660219

Cited by 23 publications

(21 citation statements)

References 8 publications

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“…This is certified, by examining By comparing our results to some already developed methods for automatic road-sign detection and recognition, the proposed technique ensures better detection and classification rates, in most of the examined cases. For example, in Barnes and Zelinsky (2004) the detection accuracy was 90% and the classification accuracy 75%; in Shneier (2005) the rates were 88% and 78% respectively; and in Medici et al (2008) the rates were 74% and 89.1%. Concerning the proposed methodology and especially during the categorization procedure, HOG descriptors are integrated, which lead to very satisfactory results, when compared to ordinary classification procedures.…”

Section: Overview Of the Proposed Systemmentioning

confidence: 97%

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Adam¹,

Ioannidis²

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper examines the detection and classification of road signs in color-images acquired by a low cost camera mounted on a moving vehicle. A new method for the detection and classification of road signs is proposed based on color based detection, in order to locate regions of interest. Then, a circular Hough transform is applied to complete detection taking advantage of the shape properties of the road signs. The regions of interest are finally represented using HOG descriptors and are fed into trained Support Vector Machines (SVMs) in order to be recognized. For the training procedure, a database with several training examples depicting Greek road sings has been developed. Many experiments have been conducted and are presented, to measure the efficiency of the proposed methodology especially under adverse weather conditions and poor illumination. For the experiments training datasets consisting of different number of examples were used and the results are presented, along with some possible extensions of this work.

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Section: Overview Of the Proposed Systemmentioning

confidence: 97%

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Adam¹,

Ioannidis²

2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…10. We did not apply the dilation over the whole image from the beginning like in other researches [14], in order to save a lot of processing time. By using the structuring elements we are able to fill up to 3 pixels gap by applying this algorithm only once.…”

Section: Problem Of Sign Fragmentationmentioning

confidence: 99%

Real-Time Recognition of Blue Traffic Signs Designating Directions

Alsibai

Hirai

2010

Int. J. ITS Res.

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In this research we propose a real-time system to recognize blue traffic signs designating directions. This research is complementary to the previous work done on six annular red signs. The system consists of several processing steps: We firstly label the blue objects in each frame and segment them from the background. After that we try to verify if the segmented blue object is a sign candidate, and then we segment white objects within the blue object. Finally we classify the white objects by matching them to arrow patterns according to geometrical features, or reject them if no arrow pattern is matched. Classification is done using a decision tree. Processing time is about 110 ms/frame, and recognition rate is about 81%.

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“…There are several similar systems: 'Opel Eye' of Opel, 'Speed Limit Assist' from the company Mercedes-Benz, 'Traffic Sign Recognition', Ford and others. Most of them are aimed at the detection and recognition of road signs limiting the velocity of movement [1].…”

Section: Introductionmentioning

confidence: 99%

Traffic Signs Detection Using Tracking with Prediction

Yakimov

2016

E-Business and Telecommunications

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Abstract. This paper proposes an efficient algorithm for real-time traffic sign detection. The article considers the practicability of using HSV color space to extract the red color. An algorithm to remove noise to improve the accuracy and speed of detection was developed. A modified Generalized Hough transform is then used to detect traffic signs. The current velocity of a vehicle is used to predict the sign's location in the adjacent frames in a video sequence. Finally, the detected objects are being classified. The detection and classification of road signs algorithms are implemented using CUDA and operate in real time on an Android device. The developed algorithms have been tested using real scene images and the German Traffic Sign Detection Benchmark (GTSDB) dataset and showed efficient results.

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Road sign detection and recognition

Cited by 23 publications

References 8 publications

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Automatic road sign detecion and classification based on support vector machines and HOG descriptos

Real-Time Recognition of Blue Traffic Signs Designating Directions

Traffic Signs Detection Using Tracking with Prediction

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