scholarly journals An Approach towards Service System Building for Road Traffic Signs Detection and Recognition

2018 ◽  
Vol 141 ◽  
pp. 64-71 ◽  
Author(s):  
Natalia Kryvinska ◽  
Aneta Poniszewska-Maranda ◽  
Michal Gregus
Keyword(s):  
Service System ◽  
Road Traffic ◽  
Traffic Signs ◽  
System Building ◽  
Detection And Recognition

scholarly journals Urban Road Traffic Sign Detection & Recognition with Time Space Relationship Model

2021 ◽  
Vol 4 (1) ◽  
pp. 22-33
Author(s):  
Bhutto Jaseem Ahmed ◽  
Qin Bo ◽  
Qu Jabo ◽  
Zhai Xiaowei ◽  
Abdullah Maitlo
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Urban Road ◽  
Time Space ◽  
Sign Detection ◽  
Relationship Model ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Detection and recognition of urban road traffic signs is an important part of the Modern Intelligent Transportation System (ITS). It is a driver support function which can be used to notify and warn the driver for any possible incidence on the current stretch of road. This paper presents a robust and novel Time Space Relationship Model for high positive urban road traffic sign detection and recognition for a running vehicle. There are three main contributions of the proposed framework. Firstly, it applies fast color-segment algorithm based on color information to extract candidate areas of traffic signs and reduce the computation load. Secondly, it verifies the traffic sign candidate areas to decrease false positives and raise the accuracy by analysing the variation in preceding video-images sequence while implementing the proposed Time Space Relationship Model. Lastly, the classification is done with Support Vector Machine with dataset from real-time detection of TSRM. Experimental results indicate that the accuracy, efficiency, and the robustness of the framework are satisfied on urban road and detect road traffic sign in real time.

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

2018 ◽  
Vol 14 (03) ◽  
pp. 34 ◽  
Author(s):  
Xianyan Kuang ◽  
Wenbin Fu ◽  
Liu Yang
Keyword(s):  
Real Time ◽  
Random Forests ◽  
Road Traffic ◽  
Contrast Ratio ◽  
Region Of Interest ◽  
Traffic Sign ◽  
Data Set ◽  
Traffic Signs ◽  
Real Time Detection ◽  
Detection And Recognition

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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