Open AccessVehicle detection based on point cloud intensity and distance clustering
Author(s) -
Wang Zhao,
Xing Wang,
Bin Fang,
Kun Yu,
Jie Ma
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1748/4/042053
Subject(s) - point cloud , cluster analysis , computer science , lidar , segmentation , artificial intelligence , obstacle , cloud computing , computer vision , euclidean distance , point (geometry) , remote sensing , classifier (uml) , pattern recognition (psychology) , data mining , mathematics , geography , operating system , geometry , archaeology
In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.