Optimizing Moving Object Trajectories from Roadside Lidar Data by Joint Detection and Tracking

Abstract

High-resolution traffic data, comprising trajectories of individual road users, are of great importance to the development of Intelligent Transportation Systems (ITS), in which they can be used for traffic microsimulations and applications such as connected vehicles. Roadside laser scanning systems are increasingly being used for tracking on-road objects, for which tracking-by-detection is the widely acknowledged method; however, this method is sensitive to misdetections, resulting in shortened and discontinuous object trajectories. To address this, a Joint Detection And Tracking (JDAT) scheme, which runs detection and tracking in parallel, is proposed to mitigate miss-detections at the vehicle detection stage. Road users are first separated by moving point semantic segmentation and then instance clustering. Afterwards, two procedures, object detection and object tracking, are conducted in parallel. In object detection, PointVoxel-RCNN (PV-RCNN) is employed to detect vehicles and pedestrians from the extracted moving points. In object tracking, a tracker utilizing the Unscented Kalman Filter (UKF) and Joint Probabilistic Data Association Filter (JPDAF) is used to obtain the trajectories of all moving objects. The identities of the trajectories are determined from the results of object detection by using only a certain number of representatives for each trajectory. The developed scheme has been validated at three urban study sites using two different lidar sensors. Compared with a tracking-by-detection method, the average range of object trajectories has been increased by >20%. The approach can also successfully maintain continuity of the trajectories by bridging gaps caused by miss-detections.

Object Detection Based on Roadside LiDAR for Cooperative Driving Automation: A Review

“…(2) Object point cloud segmentation and recognition: After filtering the background point cloud, it is necessary to further identify vehicles, pedestrians, and other objects from the filtered foreground point cloud. Firstly, a three-dimensional object point cloud clustering algorithm, such as the point cloud clustering method based on Euler distance [ 55 , 56 , 57 , 58 ], point density, and its variants [ 36 , 43 , 44 , 59 , 60 , 61 , 62 ], is used to accurately segment the foreground object point cloud into independent objects. Then, according to the prior knowledge of the object, several handcrafted features, such as the standard deviation and clustering dimension of the cluster point cloud, are extracted from the cluster.…”

“…Then, the non-background point cloud is used as the input of the object detection network (e.g., PointPillars [ 79 ], SECOND [ 84 ], TANet [ 85 ]) for the training and testing of the model. Zhang et al [ 57 ] adopted a similar approach for roadside LiDAR object detection and utilized PointVoxel-RCNN [ 86 ] (PV-RCNN) to detect vehicles and pedestrians from the extracted moving points. The experimental results show that the generalization ability of the above detection methods for target detection in different scenes has been improved.…”

Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments

“…Lidar, especially the time-of-flight (TOF) variant, is widely used in autonomous vehicles to acquire point cloud data, aiding in object detection, localization, and map construction. Integrating lidar into roadside infrastructure is emerging, and some studies have been conducted using roadside lidar [1][2][3][4][5][6][7][8][9][10].…”