Research on Road Traffic Sign Recognition Based on Video Image

The frequent occurrence of road congestion and traffic accidents has affected people's travel efficiency and travel safety. Traffic sign recognition has become one of the key research objects in intelligent transportation system. This paper studies the identification of road traffic signs based on video images. First of all, collected image will be image preprocessing with image reduction, brightness adjustment, filtering. Secondly, the traffic signs are segmented by means of area filtering and morphological processing based on color and shape features. The method of feature extraction of traffic signs is studied to train traffic sign samples. According to the characteristics of many kinds of traffic signs, select linear kernel function and combine one to one SVM classifier to classify traffic signs. Finally, the traffic signs are identified based on Matlab software. The results show that the traffic signs can be accurately identified.

[1]  Bin Ran,et al.  Vision-based object detection and recognition system for intelligent vehicles , 1999, Other Conferences.

[2]  Yoshiaki Shirai,et al.  An active vision system for real-time traffic sign recognition , 2000, ITSC2000. 2000 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.00TH8493).

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  Huchuan Lu,et al.  Sample-Specific SVM Learning for Person Re-identification , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Qiu Ju Image Processing Based on VC.NET , 2005 .

[6]  Bin Ran,et al.  Vision-Based Stop Sign Detection and Recognition System for Intelligent Vehicles , 2001 .

[7]  Gang Wang,et al.  Detection of geometric shape for traffic lane and mark , 2012, 2012 IEEE International Conference on Information and Automation.

[8]  Zhilu Wu,et al.  A hierarchical method for traffic sign classification with support vector machines , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[9]  Cheng-Lin Liu,et al.  Traffic Sign Detection Using a Cascade Method With Fast Feature Extraction and Saliency Test , 2017, IEEE Transactions on Intelligent Transportation Systems.