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.

翻译：背景建模广泛应用于智能监控系统，通过减去静态背景成分来检测运动目标。大多数路边激光雷达目标检测方法基于多帧数据的描述性统计（如体素密度、邻域点数、最大距离），通过将新数据点与预训练的背景参考进行对比，来滤除前景点。然而，这些解决方案在重交通场景下效率低下，且参数值难以在不同场景间迁移。早期研究中，基于概率的背景建模方法虽广泛用于视频系统，但因点云数据的稀疏性和非结构化特性，被认为不适用于路边激光雷达监控系统。本文基于每个激光雷达点的仰角和方位角，将原始激光雷达数据转换为结构化表示。借助这种高阶张量表示，我们突破了高效高维多元分析的障碍，实现了路边激光雷达背景建模。贝叶斯非参数（BNP）方法融合强度值与三维测量数据，充分利用三维信息和强度信息进行测量数据分析。所提方法与两种最先进的路边激光雷达背景模型、计算机视觉基准模型以及深度学习基线模型进行了对比，在重交通和恶劣天气条件下，从点级、目标级和路径级三个层面进行评估。这种多模态加权贝叶斯高斯混合模型（GMM）能够处理带有噪声测量的动态背景，显著增强了基于基础设施的激光雷达目标检测能力，从而可创建面向智慧城市应用的各种三维建模。