Detection and recognition of traffic signs using Gabor filters

Mariut, Felix; Foşalău, Cristian; Avila, Manuel; Petrisor, Daniel

doi:10.1109/tsp.2011.6043668

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…In TSR work, majority of state-of-the-art methods depends on handcrafted features, e.g. SIFT [26], [27], HOG [4], [28]- [33], color global and local-oriented edge magnitude pattern [34], Gabor [35] or an amalgamation of these features included by coding procedure [36]. In any of the cases, the recognition accuracy of these techniques isn't gratifying enough as there is discrimination of the existing handcrafted features.…”

Section: Related Workmentioning

confidence: 99%

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

Mahmoud

Guo

2019

IEEE Access

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This paper centers on a novel method for traffic sign recognition (TSR). The method comprises of two major steps: 1) make strong representations for TSR images, by extraction deep features with the deep convolutional generative adversarial networks (DCGANs) and 2) classifier defined by multilayer perceptron (MLP) neural networks trained with a pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE training process is considered efficient in which it does not require the number of hidden layers specified nor does it need the setting of the learning control parameters. This results in the PILAE classifier attaining a better performance in terms of both accuracy and efficiency. Empirical results from the German TSR (GTSRB) and Belgium traffic sign classification (BTSC) have proved that TSR achieves excellent results with other algorithms and reasonably low complexity. INDEX TERMSDeep convolutional generative adversarial networks (DCGAN), feature extraction, pseudoinverse learning autoencoder (PILAE), traffic sign recognition (TSR).

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Section: Related Workmentioning

confidence: 99%

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

Mahmoud

Guo

2019

IEEE Access

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“…This detection stage is performed and followed by some form of shape detection and recognition. In the recognition stage, detected candidates are either rejected or identified with some recognition methods, for example, shape matching [ 32 ] and some other form of classifiers such as ANN [ 15 ], support vector machine (SVM) [ 7 , 33 , 34 ], clustering [ 35 ], and fuzzy logic [ 9 ].…”

Section: Related Workmentioning

confidence: 99%

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Islam

Raj

2017

Sensors

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Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

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“…Therefore, some authors (Berkaya, Gunduz, Ozsen, Akinlar & Gunal, 2016) used a color ratio between the intensity of RGB components, while others (de la Escalera, Moreno, Salichs & Armingol, 1997) used only one RGB component as a reference to detect the sign colors in the image. To reduce the dependency on illumination variation, the Hue Saturation Intensity (HSI) system (Han, Virupakshappa & Oruklu, 2015) and HSV (Mariut, Fosalau, Avila & Petrisor, 2011) has been frequently used.…”

Section: Figure 1 Traffic Signs Detection Challengesmentioning

confidence: 99%

Automatic Recognition of Traffic Signs with 3D Distance Estimation for Intelligent Vehicles

Romdhane

Mliki

Hammami

2017

International Journal of Software Innovation

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Traffic sign recognition (TSR) systems within the Advanced Driving Assistance Systems (ADAS) has attract many researchers as it plays a vital role to ensure road and transportation safety. In this paper, the authors introduce a new method for the automatic recognition and distance estimation of traffic signs using hybrid 2D-3D approach. The proposed method performs on two steps: Traffic signs recognition and distance estimation. The traffic signs recognition step aims to detect, classify and track traffic signs. Regarding the distance estimation step, it seeks to compute the depth distance of the traffic sign so as to estimate the distance between the recognized traffic sign and the vehicle carrying the camera. Many series of experiments were carried out on GTSBD and KITTI Stereo 2015 databases to evaluate the performance of the authors' method. The obtained results have shown that the proposed method achieves good performance in challenging scenarios.

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Detection and recognition of traffic signs using Gabor filters

Cited by 12 publications

References 7 publications

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

A Novel Method for Traffic Sign Recognition Based on DCGAN and MLP With PILAE Algorithm

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Automatic Recognition of Traffic Signs with 3D Distance Estimation for Intelligent Vehicles

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