基于多尺度特征表示的城市道路检测  

Urban Road Detection Based on Multi-scale Feature Representation

作　　者：李骏扬[1] 金立左[1] 费树岷[1] 马军勇 

机构地区：[1]东南大学自动化学院,南京210096 [2]光电控制技术重点实验室,洛阳471009

出　　处：《电子与信息学报》2014年第11期2578-2585,共8页Journal of Electronics & Information Technology

基　　金：航空科学基金(20115169016);国家省部级基金;江苏省自然科学基金(BK20131296)资助课题

摘　　要：基于图像的车辆周边场景分析是近来车辆主动安全的热门研究方向,但对于复杂路况的道路识别目前依然是一个难题。该文提出一种适用于城市复杂道路场景的单目视觉路面识别算法。该方法结合多尺度的稀疏编码,在大尺度上利用道路的局部纹理信息,在较小尺度,特别是中等尺度上利用空间上下文信息,对车辆的可行驶区域进行识别。实验表明,该方法提高了道路与周边环境中相似纹理的区分能力;在铺设良好的结构化道路,或者车道线、路界缺失,光照复杂的道路场景中,该方法都取得了较好的检测结果。Vision-based road detection is a popular area in research of driving security, however, detecting in complex road scenery is still a challenging topic. An approach is proposed to detect drivable road region from monocular images in urban environments. The algorithm is based on multi-scale sparse representation, with local texture in large scale, and context in medium scale. Experiments show that, distinguishing the similar texture of pavements from that of surrounding buildings and obstacles brings a well-performance in structured roads as well as the diverse road environments such as lack of lanes or clear boundaries but full of complex illuminations.

关 键 词：模式识别 路面识别 路面纹理 多尺度稀疏表示 K奇异值分解(K-SVD) 正交匹配追踪(OMP) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]