Urban vehicle extraction from aerial laser scanning point cloud data

A vehicle extraction method is proposed in this paper to extract vehicles in urban areas more accurately from airborne point clouds. First, the ground points are separated from the non-ground points, and a potential vehicle-occupied area (PVOA) is then extracted from the ground point cloud. A PVOA-b...

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Bibliographic Details
Published in:International journal of remote sensing 2020-09, Vol.41 (17), p.6664-6697
Main Authors: Zhang, Tonggang, Kan, Yuhui, Jia, Hailong, Deng, Chuan, Xing, Tingsong
Format: Article
Language:English
Subjects:
Algorithms
Image segmentation
Lasers
Object recognition
Shape
Shape recognition
Urban areas
Vehicles
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Summary:A vehicle extraction method is proposed in this paper to extract vehicles in urban areas more accurately from airborne point clouds. First, the ground points are separated from the non-ground points, and a potential vehicle-occupied area (PVOA) is then extracted from the ground point cloud. A PVOA-based non-ground point cloud segmentation method is proposed in this work, and a gap-based method is put forward to re-cluster the segment, which may include multiple vehicles. The non-ground point cloud is clustered into a series of one-vehicle segments and empty segments. Following this, a shape-based vehicle recognition method is presented that can judge whether or not a given segment is a vehicle using a dynamic time warping similarity measurement. In addition to judging whether or not a segment is a vehicle, the category of each vehicle can also be determined. To a significant extent, our PVOA-based non-ground point cloud segmentation method can avoid the difficulties of over- and under-segmentation that arise in current mainstream methods of object-based vehicle extraction, and can also avoid incorrect segmentation in case of vehicles parked close together. Our shape-based vehicle recognition method can exclude non-vehicle objects, especially those with sizes similar to those of vehicles. This method is more effective than current algorithms based on normal geometric features such as area and rectangularity. Using four datasets of typical urban scenarios, the performance of the new algorithm is tested and compared with that of the OBPCA and DT algorithm. The experimental results show that the correctness, completeness, and quality of the new algorithm are 96.7%, 91.1%, and 93.8%, higher than that of the OBPCA and DT algorithm.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431161.2020.1742947