LIDAR and vision-based real-time traffic sign detection and recognition algorithm for intelligent vehicle

2014 ◽  
Author(s):  
Lipu Zhou ◽  
Zhidong Deng
Keyword(s):  
Real Time ◽  
Recognition Algorithm ◽  
Intelligent Vehicle ◽  
Traffic Sign ◽  
Real Time Traffic ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

An Approach of Traffic Sign Recognition Algorithm on MATLAB

2014 ◽  
Vol 644-650 ◽  
pp. 3980-3983
Author(s):  
Jia Yang Li ◽  
Mei Xia Song
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

scholarly journals Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3192 ◽  
Author(s):  
Faming Shao ◽  
Xinqing Wang ◽  
Fanjie Meng ◽  
Ting Rui ◽  
Dong Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Gabor Wavelets ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Automatic Driving ◽  
Real Time Traffic ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition ◽  
Grayscale Images

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

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