Background Filtering and Object Detection with Roadside LiDAR Data

The roadside LiDAR(light detection and ranging) sensors can obtain relevant information about all roadway users by collecting real-time 3D point cloud data of surrounding objects. In order to analyze the point cloud data of roadway users, background filtering and object detection are essential steps. This paper proposes a background filtering method based on point correlation by KDTree neighborhood searching and an adaptive threshold Euclidean clustering method. In order to improve the detection speed, background filtering will be performed on the original point cloud data before Euclidean clustering, so as to delete a large number of useless background points, and the clustering results will be distinguished by cuboid frame markers. The experimental results show that the single-frame point cloud clustering speed has increased by 31.3%, the clustering speed has been significantly improved.