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中文摘要：
根据交通信号灯灯板颜色和形状定位图像中的灯板位置．将灯板区域的彩色图像转换到YCbCr空间，分割灯板区域中的红、黄、绿三色区域，利用交通信号灯的形态特性定位交通灯位置．用Gabor小波和2维独立分量分析提取感兴趣区域的特征，送入最近邻分类器分类信号灯的类型．用代表性的观测序列建立隐马尔科夫模型，并结合识别和跟踪结果估计信号灯状态．实验结果表明，该算法能可靠、准确地识别出信号灯，并有效地估计出信号灯的状态．
英文摘要：
The board of traffic light is located in input image by its color and shape. The color image of the board is converted to YCbCr space. The region that includes main color（red, amber, green） is segmented by thresholds. The traffic light is positioned by morphological properties. The feature of region of interest（RoI） is extracted by Gabor wavelet and 2 dimension independent component analysis, and sent into the nearest neighbor classifier to classify the type of traffic light. Structures of hidden Markov model are built by several representative observation sequences. The current state of traffic light is estimated by hidden Markov model which combines with recognition and tracking results. The experimental results show that this algorithm can recognize and track the traffic lights reliably and accurately, and estimate the states of the traffic light effectively.
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