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In this paper, we developed the solution of roadside LiDAR object detectionusing a combination of two unsupervised learning algorithms. The 3D pointclouds are firstly converted into spherical coordinates and filled into theelevation-azimuth matrix using a hash function. After that, the raw LiDAR datawere rearranged into new data structures to store the information of range,azimuth, and intensity. Then, the Dynamic Mode Decomposition method is appliedto decompose the LiDAR data into low-rank backgrounds and sparse foregroundsbased on intensity channel pattern recognition. The Coarse Fine TriangleAlgorithm (CFTA) automatically finds the dividing value to separate the movingtargets from static background according to range information. After intensityand range background subtraction, the foreground moving objects will bedetected using a density-based detector and encoded into the state-space modelfor tracking. The output of the proposed solution includes vehicle trajectoriesthat can enable many mobility and safety applications. The method was validatedat both path and point levels and outperformed the state-of-the-art. Incontrast to the previous methods that process directly on the scattered anddiscrete point clouds, the dynamic classification method can establish the lesssophisticated linear relationship of the 3D measurement data, which capturesthe spatial-temporal structure that we often desire.

Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction