| 高分辨率航空影像中斑马线的识别与重建 |
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| 引用本文： | 杨冲,张帆,王健,黄先锋,高云龙.高分辨率航空影像中斑马线的识别与重建[J].武汉大学学报(信息科学版),2017,42(10):1358. |
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| 作者姓名： | 杨冲 张帆 王健 黄先锋 高云龙 |
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| 作者单位： | 1.武汉大学遥感信息工程学院, 湖北 武汉, 430079 |
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| 基金项目： | 国家科技支撑计划2014BAK07B04国家自然科学基金41571437浙江省科技计划2015C33075 |
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| 摘 要： | 提出了一种利用高分辨率航空影像自动识别与重建斑马线的方法。文中利用基于灰度共生矩阵（cray level co-occurrence matrix，GLCM）和二维Gabor滤波器特征的JointBoost分类器来提取斑马线，并依据斑马线在空间几何上的重复性规则对斑马线建立参数模型。最后结合一些具有代表性的实验数据（如阴影、遮挡和模糊等）来验证本文所提出的方法在斑马线的识别与重建中的有效性。
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| 关 键 词： | 斑马线 灰度共生矩阵 Gabor滤波器 JointBoost 参数模型 |
| 收稿时间： | 2016-12-13 |
Extraction and Reconstruction of Zebra Crossings from High Resolution Aerial Images |
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| Affiliation: | 1.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | Zebra Crossings have played an important role in public traffic safety, so the reconstruction of zebra crossings is very helpful for reducing the occurrence of traffic accidents. An automatic approach using high-resolution aerial images for zebra crossing extraction and reconstruction is proposed in this paper. In the approach, zebra crossings are extracted by JointBoost classifier based on GLCM (Gray Level Co-occurrence Matrix) features and 2D Gabor Features. A geometric parameter model based on spatial repeatability relationships is globally fitted to reconstruct the geometric shapes of zebra crossings. Representative experiments under interfered conditions such as zebra crossings covered by pedestrians, shadows and color fading were conducted to verify the validity of the proposed method in both the extraction and reconstruction of zebra crossings. |
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