Recognition Of Road Traffic Signs In Natural Environments

With the development of society and economy, the road transport industry in China has been developed continuously and rapidly. Highly developed modern transportation provides the convenience to human, however, a series of issues, including the traffic safety and traffic congestion, are becoming increasing serious. In order to solve these problems, the Intelligent Traffic System(ITS) Application is come into being, and the road Traffic Sign Recognition system(TSR), as a sub-topic of ITS research field, has become a focus to domestic and foreign scholars. The TSR system can be descript as follows:The natural scene images shot with a camera mounted on a vehicle, are sent to the image processing module for image-understanding, traffic sign detection and recognition, then informs drivers of the final results, as a result, the traffic safety can be enhanced and the traffic jams can be reduced.The warning signs, the prohibition signs, and the directional signs are the most important and common types, there are specific colors and shapes to distinguish them from other objects and remind drivers or pedestrians. More than a decade, the research of road Traffic Sign Recognition has been progressed highly and achieved some results, but the complexity of the background, light, and so on, led to its research more challengeable than the target-recognition in non-natural scenes. The factors are mainly as follows:light-condition and its uncontrollable feature, image-blurring because of vehicle vibration, damaged, contaminated or blocked, faded, weather of rain and fog.etc, projection distortion, scale transformation, titled, same or similar color-background.An intelligent algorithm of detection for the traffic signs based on the color information and the shape features was proposed. It consists of two parts, the first one is the image-segmentation for traffic signs with the HSV(Hue-Saturation-Value) color space, and the other one is the detection for traffic signs using the color information and the shape features. In this algorithm, RGB(Red-Green-Blue) images were converted into HSV color space, then the Region of Interests(ROIs) were located by extracting the thresholds of different types of colors. Based the geometry of ROIs, then, warning signs, prohibition signs and directional signs were divided, in consequence, traffic sign detection was done. In order to overcome several of unfavorable factors which are generally exist in the natural scenes, an image enhancement algorithm based on multi-scale Retinex and two correction approaches for the traffic signs are proposed. The first correction approach is the Affine transformation for the triangular traffic signs, and the second one is the normalization for the circle traffic signs and the rectangular traffic signs. Experiments were conducted for traffic sign detection and the results indicated that this intelligent algorithm could overcome types of unfavorable factors and posses of good robustness, which verified its effectiveness.Support Vector Machine(SVM) is a new self-learning algorithm, which built on the Statistical Leaning Theory and the Structural Risk Minimization principle, it shows good advantages in small-sample-pattern-recognition(classification). Recognition and classification of traffic signs are always conducted in limited samples, and taking this into consideration, it presents an approach for traffic signs recognition based on SVM. Hu-Invariant-Moments and Zernike-Invariant-Moments represent different feature data of different traffic signs, and it is compared that the accuracy of traffic signs classification and recognition with two Support Vector Machines:C-SVM and v-SVM in different types of kernel functions, Linear kernel, Polynomial kernel, RBF kernel and Sigmoid kernel. And for every kernel function, its penalty-factor was adjusted to the optimal. The different Limited-Speed traffic signs experiments were conducted for traffic sign classification and recognition after feature data normalization and kernel function optimization, and the good results achieved.