GC-Net: Gridding and Clustering for Traffic Object Detection With Roadside LiDAR
Liwen Zhang, Jianying Zheng, Rongchuan Sun, Yanyun Tao · IEEE Intelligent Systems · 2020
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.