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

Lookup NU author(s): Dr Jiaxing Zhang, Professor Jon MillsORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 by the authors. Licensee MDPI, Basel, Switzerland. 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.

Publication metadata

Author(s): Zhang J, Xiao W, Mills JP

Publication type: Article

Publication status: Published

Journal: Remote Sensing

Year: 2022

Volume: 14

Issue: 9

Online publication date: 28/04/2022

Acceptance date: 26/04/2022

Date deposited: 23/05/2022

ISSN (electronic): 2072-4292

Publisher: MDPI


DOI: 10.3390/rs14092124


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