Based on the fusion of features traffic sign detecting

In the detection of road traffic signs ,this study proposes a road traffic signs feature detection methods based on many featured fusion in image, which carefully analysis the characteristics and problems of road traffic signs, for example, images in real traffic road traffic signs detecting are always distorted and the size of signs as well as position is uncertain. The samples of study are divided into training samples and testing samples. Firstly, the study make a blind restoration process with the images of training sample, Secondly, we cut the recovered images according to their own color, texture and shape features. Thirdly, color, texture and shape features are respectively detected with SVM classification, so we can get these classification models respectively. At last, the study get a weighted feature fusion model which the weighted value of color, texture and shape features for the adaptive weighted feature fusion model. After the model is tested by these test samples, the results show that the feature fusion recognition method has a very high accuracy. In addition, the contrast data of the comparative experiments show that fused features can effectively improve the accuracy and robustness of traffic detecting.