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Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction. (arXiv:2201.04756v1 [cs.CV])
Jan. 14, 2022, 2:10 a.m. | Tianya Zhang, Peter J. Jin
cs.CV updates on arXiv.org arxiv.org
In this paper, we present the solution of roadside LiDAR object detection
using a combination of two unsupervised learning algorithms. The 3D point
clouds data are firstly converted into spherical coordinates and filled into
the azimuth grid matrix using a hash function. After that, the raw LiDAR data
were rearranged into spatial-temporal data structures to store the information
of range, azimuth, and intensity. Dynamic Mode Decomposition method is applied
for decomposing the point cloud data into low-rank backgrounds and sparse …
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