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

arXiv: https://arxiv.org/abs/2201.04756 · HTML: https://mlsanity.com/paper/2201.04756/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 13 Jan 2022.

## Builds on (most cited first, 13 of 13)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018)
- [VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection](https://mlsanity.com/paper/1711.06396/) (2017)
- [Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud](https://mlsanity.com/paper/2003.01251/) (2020)
- [SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud](https://mlsanity.com/paper/1710.07368/) (2017)
- [SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud](https://mlsanity.com/paper/1809.08495/) (2018)
- [PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving](https://mlsanity.com/paper/2112.12610/) (2021)
- [Vehicle Detection from 3D Lidar Using Fully Convolutional Network](https://mlsanity.com/paper/1608.07916/) (2016)
- [SuMa++: Efficient LiDAR-based Semantic SLAM](https://mlsanity.com/paper/2105.11320/) (2021)
- [BirdNet: a 3D Object Detection Framework from LiDAR information](https://mlsanity.com/paper/1805.01195/) (2018)
- [Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks](https://mlsanity.com/paper/1805.08689/) (2018)
- [ChipNet: Real-Time LiDAR Processing for Drivable Region Segmentation on an FPGA](https://mlsanity.com/paper/1808.03506/) (2018)
- [Real-Time Road Segmentation Using LiDAR Data Processing on an FPGA](https://mlsanity.com/paper/1711.02757/) (2017)

## Built on by (most cited first, 7 of 7)

- [InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors](https://mlsanity.com/paper/2305.00314/) (2023)
- [A Ground Segmentation Method Based on Point Cloud Map for Unstructured Roads](https://mlsanity.com/paper/2309.08164/) (2023)
- [Progressive Bird's Eye View Perception for Safety-Critical Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2508.07560/) (2025)
- [A Fully Interpretable Statistical Approach for Roadside LiDAR Background Subtraction](https://mlsanity.com/paper/2510.22390/) (2025)
- [Weighted Bayesian Gaussian Mixture Model for Roadside LiDAR Object Detection](https://mlsanity.com/paper/2204.09804/) (2022)
- [Beam-Wise Statistical Background Subtraction for Static Roadside LiDAR: A Cross-Sensor Benchmark Study](https://mlsanity.com/paper/2608.14868/) (2026)
- [Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation](https://mlsanity.com/paper/2609.05523/) (2026)

## Abstract (arXiv)

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.