  In this paper, we developed the solution of roadside LiDAR object detection
using a combination of two unsupervised learning algorithms. The 3D point
clouds are firstly converted into spherical coordinates and filled into the
elevation-azimuth matrix using a hash function. After that, the raw LiDAR data
were rearranged into new data structures to store the information of range,
azimuth, and intensity. Then, the Dynamic Mode Decomposition method is applied
to decompose the LiDAR data into low-rank backgrounds and sparse foregrounds
based on intensity channel pattern recognition. The Coarse Fine Triangle
Algorithm (CFTA) automatically finds the dividing value to separate the moving
targets from static background according to range information. After intensity
and range background subtraction, the foreground moving objects will be
detected using a density-based detector and encoded into the state-space model
for tracking. The output of the proposed solution includes vehicle trajectories
that can enable many mobility and safety applications. The method was validated
at both path and point levels and outperformed the state-of-the-art. In
contrast to the previous methods that process directly on the scattered and
discrete point clouds, the dynamic classification method can establish the less
sophisticated linear relationship of the 3D measurement data, which captures
the spatial-temporal structure that we often desire.
