  Background modeling is widely used for intelligent surveillance systems to
detect moving targets by subtracting the 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 widely
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
representation based on the elevation and azimuth value of each LiDAR point.
With this high-order tensor representation, we break the barrier to allow
efficient high-dimensional multivariate analysis for roadside 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. 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 Weighted Bayesian
Gaussian Mixture Model (GMM) can handle dynamic backgrounds with noisy
measurements and substantially enhances the infrastructure-based LiDAR object
detection, whereby various 3D modeling for smart city applications could be
created.
