城市环境中箭头型交通信号灯的实时识别算法
来源期刊：中南大学学报(自然科学版)2013年第4期
论文作者：谷明琴 蔡自兴 黄振威 何芬芬
文章页码：1403 - 1408
关键词：交通信号灯识别；Gabor小波变换；二维独立分量分析；最近邻分类器
Key words：traffic light recognition; Gabor wavelet transform; 2D independent component analysis; nearest neighbor classifier
摘 要：提出一种检测和识别城市环境中箭头型交通信号灯的新方法。首先，用图像颜色分割和形态滤波来定位交通信号灯的灯板位置；其次，将交通信号灯的灯板区域彩色图像转换到YCbCr空间，对Cb和Cr通道进行阈值分割，判断形态及交通信号灯与灯板的相对位置来确定红色、黄色和绿色交通信号灯候选区域。然后，用二维Gabor小波变换和二维独立分量分析来提取交通信号灯候选区域的特征；最后，用最近邻分类器识别交通信号灯的箭头方向。实验结果表明：该算法的总体识别率超过91%，每帧图像的处理时间为152 ms，能够为行驶的车辆提供实时、稳定和准确箭头型交通信号灯信息。
Abstract: A novel approach for detecting and recognizing arrow traffic lights in urban scenes was proposed. Firstly, the boards of arrow traffic lights were localized by image segmentation and morphology processing. Secondly, color image of traffic light board region was converted to YCbCr color space. Candidates of traffic lights (red, yellow, green) were obtained through threshold segmentation in Cb and Cr channels, by judging morphology and relative position between candidate and its board. Thirdly, Gabor wavelet transform and 2D independent component analysis (2DICA) were used to extract traffic light candidate’s features, and finally nearest neighbor classifier identifies arrow direction. Experimental results indicate that the overall recognition rates of the proposed method are over 91%, and computation time of each frame is 152 ms. So the proposed algorithm will provide real-time, robust and accurate arrow traffic lights information to moving vehicles.
谷明琴，蔡自兴，黄振威，何芬芬
(中南大学 信息科学与工程学院，湖南 长沙，410083)
摘 要：提出一种检测和识别城市环境中箭头型交通信号灯的新方法。首先，用图像颜色分割和形态滤波来定位交通信号灯的灯板位置；其次，将交通信号灯的灯板区域彩色图像转换到YCbCr空间，对Cb和Cr通道进行阈值分割，判断形态及交通信号灯与灯板的相对位置来确定红色、黄色和绿色交通信号灯候选区域。然后，用二维Gabor小波变换和二维独立分量分析来提取交通信号灯候选区域的特征；最后，用最近邻分类器识别交通信号灯的箭头方向。实验结果表明：该算法的总体识别率超过91%，每帧图像的处理时间为152 ms，能够为行驶的车辆提供实时、稳定和准确箭头型交通信号灯信息。
关键词：交通信号灯识别；Gabor小波变换；二维独立分量分析；最近邻分类器
GU Mingqin, CAI Zixing, HUANG zhenwei, HE Fenfen
(School of Information Science and Engineering, Central South University, Changsha 410083, China)
Abstract:A novel approach for detecting and recognizing arrow traffic lights in urban scenes was proposed. Firstly, the boards of arrow traffic lights were localized by image segmentation and morphology processing. Secondly, color image of traffic light board region was converted to YCbCr color space. Candidates of traffic lights (red, yellow, green) were obtained through threshold segmentation in Cb and Cr channels, by judging morphology and relative position between candidate and its board. Thirdly, Gabor wavelet transform and 2D independent component analysis (2DICA) were used to extract traffic light candidate’s features, and finally nearest neighbor classifier identifies arrow direction. Experimental results indicate that the overall recognition rates of the proposed method are over 91%, and computation time of each frame is 152 ms. So the proposed algorithm will provide real-time, robust and accurate arrow traffic lights information to moving vehicles.
Key words:traffic light recognition; Gabor wavelet transform; 2D independent component analysis; nearest neighbor classifier
