Hash-Based Gaussian Mixture Model (HGMM) for Roadside LiDAR Smart Infrastructure Applications

Background modeling is widely used for intelligent surveillance systems to detect moving targets by subtracting static background components.Most roadside LiDAR object detection methods filter out foreground points by comparing new data points to pre-trained background references based on descriptive statistics over many frames (e.g., voxel density, number of neighbors, maximum distance).However, these solutions are inefficient under heavy traffic, and parameter values are hard to transfer from one scenario to another.In early studies, the probabilistic background modeling methods commonly used for the video-based system were considered unsuitable for roadside LiDAR surveillance systems due to the sparse and unstructured point cloud data.In this paper, the raw LiDAR data were transformed into a structured format based on the elevation and azimuth value of each LiDAR point.With this tensor representation, we break the barrier to allow the efficient multivariate Gaussian Mixture Model (GMM) for LiDAR background modeling.The Bayesian Nonparametric (BNP) approach integrates the intensity value and 3D measurements to exploit the measurement data using 3D and intensity info entirely.An adaptive GMM was also implemented that can process LiDAR background modeling in real-time.The proposed method was compared against two state-of-the-art roadside LiDAR background models, computer vision benchmark, and deep learning baselines, evaluated at point, object, and path levels under heavy traffic and challenging weather.This multimodal Bayesian GMM can handle dynamic backgrounds with noisy measurements and substantially enhances the efficiency of infrastructure-based LiDAR object detection, whereby various 3D modeling for smart city applications could be developed.