Urban traffic scene understanding is the basis of traffic monitoring and safety driving assistant system. A novel approach to understanding urban traffic scene captured from a car-mounted camera is proposed based on multi-level Sigmoidal neural network. Five 3D structure features were combined with the appearance features to represent the urban traffic environment and the recognition accuracy of traffic environment was improved by utilizing multi-level Sigmoidal neural network (MSNN) to segment and recognize the input images. Tested by the public CamVid dataset, the experimental results demonstrate the efficiency of the proposed approach.%交通场景的理解是交通监控、汽车安全辅助驾驶的基础.提出一种基于多级Sigmoid神经网络的城市交通环境理解方法.将5个3D结构特征与物体外观特征相结合表征城市交通环境,为了提高交通环境识别率,采用多级Sigmoid神经网络(MSNN)进行图像分割与识别.在公共测试视频数据库CamVid dataset 进行实验,实验结果表明了该方法的有效性.
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