An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

Wali, Safat B.; Hannan, M. A.; Hussain, Aini; Samad, Salina Abdul

doi:10.1155/2015/250461

Cited by 61 publications

(33 citation statements)

References 34 publications

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“…Machine learning and statistical learning methods are considered to be more effective methods for traffic signs recognition, such as Random Forests [19] , Convolution Neural Network (CNN) [20,21] , Support Vector Machine [22] . Huang [23] proposed a variant of HOG (HOGv), which can achieve a good balance between recognition accuracy and computational speed.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Kuang¹,

Fu²,

Liu³

2018

Int. J. Onl. Eng.

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Abstract-Real-time detection and recognition of road traffic signs plays an important role in advanced driving assistance system. Typically, the region of interest (ROI) method is effective in feature extraction but inefficient because it is sensitive to illumination changes. In this paper, we propose a maximally stable extremal regions (MSER) method with image enhancement to greatly improve ROI. Firstly, we employ gray world algorithm to process original images. And then potential areas of traffic signs are obtained through increasing the image contrast ratio and extracting the image-enhanced MSER. According to the characteristic variable and the geometry moment invariants, the geometric characteristics of traffic signs are extracted to obtain the ROIs. Finally, HSV-HOG-LBP feature is constructed and the random forests algorithm is used to identify the traffic signs. The experimental results show that our proposed method show strong robustness on illumination condition and rotation scale, and achieves a good performance by experiments with actual images and German traffic sign detection benchmark (GTSDB) data set.

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Section: Related Workmentioning

confidence: 99%

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Kuang¹,

Fu²,

Liu³

2018

Int. J. Onl. Eng.

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“…Wali et al [15] proposed a method which had three main phases: the first phase was image preprocessing, the second phase was detection and the last phase was recognition. In the detection phase, they used color segmentation with shape matching.…”

Section: Related Workmentioning

confidence: 99%

“…Overall Accuracy (%) Processing Time (s) [1] 97.60 - [15] 95.71 0.43 [17] 93.60 - [24] 98.62 0.36 [35] 95.20 - [37] 92.47 - [57] 90.27 0.35 [58] 86.70 -Proposed method 99.00 0.28 Figure 15. Real-time experimental results.…”

Section: Referencementioning

confidence: 99%

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

Islam¹,

Raj

Mujtaba³

2017

Symmetry

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Abstract:The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the wellmaintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

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“…Traffic sign recognition (TSR) system is one of the popular and challenging topics in the DAS. Basically, TSR consists of two steps [3][4][5][6][7][8][9][10][11][12][13][14][15][16]: traffic sign detection and classification. Traffic sign detection is used to detect the appearance of the traffic sign on an image.…”

Section: Introductionmentioning

confidence: 99%

“…The other important factor should be considered in the classification process is the features extraction. The features commonly used are the image histogram [3], [17]; the Histogram of Oriented Gradient (HOG) [5][6][7], [10], [11], [15], [19]; the intensity of image pixels [9], [14], [16], [18]; and the haar wavelet [15].…”

Section: Introductionmentioning

confidence: 99%

Circular Traffic Sign Classification Using Hog Based Ring Partitioned Matching

Soetedjo

Somawirata

2017

International Journal on Smart Sensing and Intelligent Systems

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Abstract-This paper presents a technique to classify the circular traffic sign based-on HOG (histogram of oriented gradients) and a ring partitioned matching. The method divides an image into several ring areas, and calculates the HOG feature on each ring area. In the matching process, the weight is assigned to each ring for calculating the distance of HOG feature between tested image and reference image. The experimental results show that the proposed algorithm achieves a high classification rate of 97.8%, without the need of many prepared sample images. The results also show that the best values of the number of orientation bins and the cell size of the HOG parameters are 5 and 10 x 10 pixels respectively.

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An Automatic Traffic Sign Detection and Recognition System Based on Colour Segmentation, Shape Matching, and SVM

Cited by 61 publications

References 34 publications

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Real-Time Detection and Recognition of Road Traffic Signs using MSER and Random Forests

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

Circular Traffic Sign Classification Using Hog Based Ring Partitioned Matching

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