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http://dx.doi.org/10.17661/jkiiect.2022.15.6.471

3D Object Detection with Low-Density 4D Imaging Radar PCD Data Clustering and Voxel Feature Extraction for Each Cluster  

Cha-Young, Oh (Department of Electronic Engineering Kookmin University)
Soon-Jae, Gwon (Department of Electronic Engineering Kookmin University)
Hyun-Jung, Jung (Department of Electronic Engineering Kookmin University)
Gu-Min, Jeong (Department of Electronic Engineering, Kookmin University)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.15, no.6, 2022 , pp. 471-476 More about this Journal
Abstract
In this paper, we propose an object detection using a 4D imaging radar, which developed to solve the problems of weak cameras and LiDAR in bad weather. When data are measured and collected through a 4D imaging radar, the density of point cloud data is low compared to LiDAR data. A technique for clustering objects and extracting the features of objects through voxels in the cluster is proposed using the characteristics of wide distances between objects due to low density. Furthermore, we propose an object detection using the extracted features.
Keywords
4D Imaging Radar; Voxels; Clustering; Classification; Object detection;
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