点云
计算机科学
聚类分析
人工智能
计算机视觉
交通标志
分割
符号(数学)
支持向量机
激光扫描
模式识别(心理学)
点(几何)
数学
激光器
几何学
物理
光学
数学分析
作者
Ying Li,Jonathan Li,Yuchun Huang,Jonathon Li
标识
DOI:10.1109/igarss.2018.8519059
摘要
This paper presents a segment-based traffic sign detection method using vehicle-borne mobile laser scanning (MLS) data. This method has three steps: road scene segmentation, clustering and traffic sign detection. The non-ground points are firstly segmented from raw MLS data by estimating road ranges based on vehicle trajectory and geometric features of roads (e.g., surface normals and planarity). The ground points are then removed followed by obtaining non-ground points where traffic signs are contained. Secondly, clustering is conducted to detect the traffic sign segments (or candidates) from the non-ground points. Finally, these segments are classified to specified classes. Shape, elevation, intensity, 2D and 3D geometric and structural features of traffic sign patches are learned by the support vector machine (SVM) algorithm to detect traffic signs among segments. The proposed algorithm has been tested on a MLS point cloud dataset acquired by a Leador system in the urban environment. The results demonstrate the applicability of the proposed algorithm for detecting traffic signs in MLS point clouds.
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