Multiband Image Segmentation and Object Recognition for Understanding Road Scenes
Multiband Image Segmentation and Object Recognition for Understanding Road Scenes
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DOI：
10.1109/tits.2011.2160539
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发表时间：
2011-12-01
影响因子：
8.5
通讯作者：
Ninomiya, Yoshiki
中科院分区：
文献类型：
--
作者：
Kang, Yousun;Yamaguchi, Koichiro;Ninomiya, Yoshiki
本文提出了一种使用分层文本袋方法在道路场景中进行语义分割和对象识别的新方法。目前的驾驶辅助系统依靠多个车载摄像头来感知道路环境。该方法依赖于集成的彩色和近红外图像，并使用分层文本袋方法来识别对象的空间配置并从背景中提取上下文信息。分层文本袋的直方图与从多尺度网格窗口提取的文本连接起来，以自动学习语义分割的空间上下文。实验结果表明，该方法比传统的文本袋方法具有更好的分割精度。通过与其他场景解释系统集成，所提出的系统可用于理解道路场景以进行车辆环境感知。
This paper presents a novel method for semantic segmentation and object recognition in a road scene using a hierarchical bag-of-textons method. Current driving-assistance systems rely on multiple vehicle-mounted cameras to perceive the road environment. The proposed method relies on integrated color and near-infrared images and uses the hierarchical bag-of-textons method to recognize the spatial configuration of objects and extract contextual information from the background. The histogram of the hierarchical bag-of-textons is concatenated to textons extracted from a multiscale grid window to automatically learn the spatial context for semantic segmentation. Experimental results show that the proposed method has better segmentation accuracy than the conventional bag-of-textons method. By integrating it with other scene interpretation systems, the proposed system can be used to understand road scenes for vehicle environment perception.