CAJ | 학술논문

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。
기우도상적차량주변장경분석시근래차량주동안전적열문연구방향，단대우복잡로황적도로식별목전의연시일개난제。해문제출일충괄용우성시복잡도로장경적단목시각로면식별산법。해방법결합다척도적희소편마，재대척도상이용도로적국부문리신식，재교소척도，특별시중등척도상이용공간상하문신식，대차량적가행사구역진행식별。실험표명，해방법제고료도로여주변배경중상사문리적구분능력；재포설량호적결구화도로，혹자차도선、로계결실，광조복잡적도로장경중，해방법도취득료교호적검측결과。
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.