Automatic detection and recognition of traffic road signs for intelligent autonomous unmanned vehicles for urban surveillance and rescue

Sebanja, Ian; Megherbi, Dalila B.

doi:10.1109/ths.2010.5655078

Cited by 17 publications

(15 citation statements)

References 14 publications

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“…They achieved a 93.9% matching rate. Authors in [70] presented a classification method using Principal Component Analysis (PCA). The system was able to achieve a performance of 99.2% correct classifications of road signs.…”

Section: Recognition Methodsmentioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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Section: Recognition Methodsmentioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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show abstract

“…Adaptive Hausdorff distance based on similarity weighting is used in recognition stage. Ian Sebanja, D. B. Megherbi [11] proposesd a multi-layered hierarchical scheme containing three phases. Traffic symbol color segmentation, shape recognition and classification.…”

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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“…Sign's color information is used in color-based methods to remove non-road sign objects from the scene. While color thresholding in RGB is being used to segment road sign images ( [2], [3], [4]), other researchers use Hue Saturation Intensity (HSI) space in the segmentation process ( [5], [6]). …”

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%

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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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Automatic detection and recognition of traffic road signs for intelligent autonomous unmanned vehicles for urban surveillance and rescue

Cited by 17 publications

References 14 publications

Detection and recognition of traffic signs inside the attentional visual field of drivers

Detection and recognition of traffic signs inside the attentional visual field of drivers

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

Speed Sign Recognition using Shape-based Features

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