Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data
Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data
复制标题
用于根据移动激光扫描数据进行道路场景物体检测的上下文视觉词袋
DOI：
10.1109/tits.2016.2550798
复制
发表时间：
2016
影响因子：
8.5
通讯作者：
Chenglu Wen
中科院分区：
文献类型：
--
作者：
Yongtao Yu;Jonathan Li;Haiyan Guan;Cheng Wang;Chenglu Wen
本文提出了一种新的道路场景对象检测算法（例如，灯杆、交通路标和汽车），从用于交通相关应用的3D移动激光扫描点云数据中提取。为了描述点云对象的局部抽象特征，通过整合特征区域的空间上下文信息，生成上下文视觉词汇表。感兴趣的对象被检测的基础上的查询对象和分割的语义对象之间的上下文视觉词的袋的相似性度量。在两个数据集上的定量评价表明，该算法在检测灯杆、交通路标和汽车时，平均召回率、精确率、质量和F值分别为0.949、0.970、0.922和0.959。比较研究表明，该算法的上级性能优于其他现有的方法。
This paper proposes a novel algorithm for detecting road scene objects (e.g., light poles, traffic signposts, and cars) from 3-D mobile-laser-scanning point cloud data for transportation-related applications. To describe local abstract features of point cloud objects, a contextual visual vocabulary is generated by integrating spatial contextual information of feature regions. Objects of interest are detected based on the similarity measures of the bag of contextual-visual words between the query object and the segmented semantic objects. Quantitative evaluations on two selected data sets show that the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.949, 0.970, 0.922, and 0.959, respectively, in detecting light poles, traffic signposts, and cars. Comparative studies demonstrate the superior performance of the proposed algorithm over other existing methods.