Gu Mingqin

Notice of Retraction Traffic lights recognition based on morphology filtering and statistical classification

Detection and recognition of the traffic lights are key processes for path planning of the intelligent vehicle. In this research, a novel method is introduced to recognize the traffic lights in urban environment. Firstly, an original image is converted to a binary image by the top-hat transform and threshold segmentation to obtain the brighter regions. Then the candidate regions without satisfying condition are removed by methods of the morphology and geometry feature filtering. Furthermore, a novel recognition method is carried out based on statistical analysis with amount of traffic lights image samples. It utilizes the color feature extracted by the Hue component in the HSV color space for classifying the types of traffic lights. Amount of experiments indicate that the novel algorithm is better adapted to the complex weather conditions, and the rate of recognition is higher than 97%, as well as the time performance could achieve the requirement of real-time processing.

Traffic Sign Recognition Based on Parameter-Free Detector and Multi-modal Representation

For the traffic sign that is difficult to detect in traffic environment, a traffic sign detection and recognition is proposed in this paper. First, the color characteristics of the traffic sign are segmented, and region of interest is expanded and extracts edge. Then edge is roughly divided by linear drawing and miscellaneous points removing. Turing angle curvature is computed according to the relations between the curvature of the vertices, vertices type is classified. The standard shapes such as circular, triangle, rectangle, etch are detected by parameter-free detector. For improving recognition accuracy, two different methods were presented to classify the detected candidate regions of traffic sign. The one method was dual-tree complex wavelet transform (DT-CWT) and 2D independent component analysis (2DICA) that represented candidate regions on grayscale image and reduced feature dimension, then a nearest neighbor classifier was employed to classify traffic sign image and reject noise regions. The other method was template matching based on intra pictograms of traffic sign. The obtained different recognition results were fused by some decision rules. The experimental results show that the detection and recognition rate of the proposed algorithm is higher for conditions such as traffic signs obscured, uneven illumination, color distortion, and it can achieve the effect of real-time processing.