GC-Net: Gridding and Clustering for Traffic Object Detection With Roadside LiDAR

Abstract

The emerging intelligent transportation systems puts higher demands on the collection and analysis of the traffic data. LiDAR can provide high-precision point clouds of traffic objects, making it a promising choice for the surveillance device. This article focuses on the traffic object detection with roadside LiDAR: estimating both positions and categories of them. To overcome the challenges posed by point clouds, we propose GC-net, which is based on a three-stage pipeline, including gridding, clustering, and classification. First, we design a one-to-one mapping on raw point cloud as data preprocessing, which transforms the data structure from the graph to the grid. Then, we propose an efficient clustering algorithm: Grid- Density-Based Spatial Clustering of Applications with Noise to search the traffic objects. It exploits index information in the grid data to simplify the computational complexity. Last, we train a CNN-based classifier to categorize the found objects by extracting the local features, which performs well even the global shapes are defective. It only employs object-wise supervision, which reduces the difficulty of creating datasets. Based on the point clouds collected in real urban traffic scenarios, comparative experiences show that the proposed GC-net achieves a superior performance both in detection accuracy and computational speed, which are significant indicators for the real-time traffic surveillance systems.