论文标题
路边激光雷达车辆的检测和跟踪使用范围和强度背景减法
Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction
论文作者
论文摘要
在本文中，我们使用两种无监督的学习算法的组合开发了路边激光镜对象检测的解决方案。首先将3D点云转换为球形坐标，并使用哈希函数填充到高度 - 齐路矩阵中。之后，将原始LIDAR数据重新排列到新的数据结构中，以存储范围，方位角和强度的信息。然后，将动态模式分解方法应用于基于强度通道模式识别的稀疏背景和稀疏前景。粗细的三角形算法（CFTA）自动找到分隔值，以根据范围信息将移动目标与静态背景区分开。强度和范围背景减法后，将使用基于密度的检测器检测前景移动对象，并编码在状态空间模型中以进行跟踪。提议的解决方案的输出包括可以实现许多移动性和安全应用的车辆轨迹。该方法在路径和点级别均已验证，并表现优于最先进的方法。与直接在分散和离散点云上进行的先前方法相反，动态分类方法可以建立3D测量数据的不太复杂的线性关系，该数据捕获了我们通常想要的时空结构。
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.
