Vehicle detection and tracking using low-channel roadside LiDAR

Traffic object detection and tracking is one of the fundamental tasks in processing point cloud data to collect high-resolution vehicle trajectories using Roadside LiDAR sensors. Comprehensive, accurate, and complete traffic object tracking within the sensor's scanning range still remains elusive, especially in complex traffic environ-ments, mainly due to challenges like occlusion. To solve these issues, a novel vehicle detection and tracking method for low-channel roadside LiDAR in complex environments is proposed. Firstly, we use the L-shape fitting method to get a more accurate bounding box to extract the object features. Then, the decision tree with bagging algorithm is used to classify the traffic objects based on the selected features. Next, an improved Hungarian algorithm with the Kalman filter is used to predict the vehicle's path considering the conditions of complete and partial occlusion. Finally, the effectiveness of the proposed framework is evaluated by comparing the roadside LiDAR data collected from four sites with the ground truth data obtained either from video-cameras or VeloView (software for point cloud data visualization). The result shows that the detection and tracking accuracy of the proposed method can reach up to 99.50% and 97%, respectively, which outperforms the state-of-the-art algorithms.

Automated summary

In this paper, a novel vehicle detection and tracking method for low-channel roadside LiDAR in complex environments is proposed. The method utilizes the L-shape fitting method to obtain accurate bounding boxes, and uses the decision tree with bagging algorithm to classify traffic objects. An improved Hungarian algorithm with the Kalman filter is applied to predict vehicle paths considering occlusion conditions. Experimental results show that the proposed method achieves detection and tracking accuracies of up to 99.50% and 97%, respectively, outperforming state-of-the-art algorithms.