Detection And Classification Of Traffic Signs In Natural Enviroments

With the development of the times, automotive vehicles gain popularity, the existing roads cannot meet the demand of traffic flux which increases gradually, and the problems such as traffic jams, accidents, road safeties and efficiencies in transportation are becoming more and more serious. Under this background, developed countries in Europe and America have been improving the existing transportation systems with high and new technique, rather than constructing more highways to expand the transportation capacity, which means that the developed countries have been developing Intelligent Transportation System (ITS). ITS is an integrated system combining the technology of communication, measurement, control and computer, and the research of intelligent vehicles based on computer vision is one of the key problem for the realization of ITS.Developing the intelligent vehicles with active safety technique has been one of the important motives of the governments, institutes and vehicle manufactures. Traffic sign recognition based on computer vision is one of the key techniques for the design of intelligent vehicles, and the system consists of two parts: one is traffic sign detection under natural scenes, the other is traffic signs classification. Although many years of studies, the robust performance of existing recognition algorithms is not good, and traffic sign recognition is still an open problem. According to this problem, the main contents of this dissertation are concentrated on the three parts: traffic sign detection, color constancy algorithm for the images of natural scenes, and traffic sign classification.A novel algorithm based on local features of region of interest is proposed for the fast detection of traffic signs in natural environments. In the algorithm, every RGB image was converted into HSV color space which was segmented by the hue and saturation thresholds, then the region of interests (ROIs) were extracted. A symmetrical detector of local binary features is proposed to extract the local features of the region of interests, and the shape of ROI is determined using a set of fuzzy rules with the local features, then the traffic signs are detected from the images under natural scenes. Experiments were conducted for the detection of traffic signs which involved 3000 images under sunny, cloudy and rainy weather conditions. The experimental results indicate that the proposed algorithm possess of high detection accuracy, and good robust performance.A modified color segmentation algorithm is presented with respect to the variations of the light conditions, weather conditions, and the paint on the signs which are the important factors resulting false detection of traffic signs. The RGB images were enhanced with color constancy algorithm, then were converted into HSV color space and segmented by the constant thresholds. Experimental results of traffic sign detection show that the segmentation algorithm is efficient with respect to the variations of the light conditions, weather conditions, and the paint on the signs, which improves the detection accuracy.A hierarchical coarse-to-fine classification scheme is proposed for traffic signs classification. In the classification module, a decision tree was designed using the features of color and shape for the coarse classification, and radial Tchebichef moments were adopted to extract the features of traffic signs, then probabilistic neural networks (PNN) were adopted for the further classification which incorporates the global K-means algorithm and Particle Swarm Optimization to improve the generalization. The experimental results demonstrate that the classification module is not only parsimonious but also has higher accuracy better generalization.After all, the traffic signs classification is a kind of classification with limited samples, Support Vector Machine (SVM) is the best tool for limited samples classification with perfect generalization. With the comprehension of SVM, C -Support Vector Classifiers andν-Support Vector Classifiers were designed for the fine classification of the hierarchical classification model. The experimental results demonstrate that the sub-classifiers based on SVM have achieved high accuracy and better generalization.