{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T15:42:28Z","timestamp":1772898148611,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T00:00:00Z","timestamp":1651104000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Infrastructure &amp; Cities: Newcastle Laboratories","award":["EP\/R010102\/1"],"award-info":[{"award-number":["EP\/R010102\/1"]}]},{"name":"Infrastructure &amp; Cities: Newcastle Laboratories","award":["201706370243"],"award-info":[{"award-number":["201706370243"]}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council Studentship","doi-asserted-by":"publisher","award":["EP\/R010102\/1"],"award-info":[{"award-number":["EP\/R010102\/1"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council Studentship","doi-asserted-by":"publisher","award":["201706370243"],"award-info":[{"award-number":["201706370243"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 &gt;20%. The approach can also successfully maintain continuity of the trajectories by bridging gaps caused by miss-detections.<\/jats:p>","DOI":"10.3390\/rs14092124","type":"journal-article","created":{"date-parts":[[2022,4,28]],"date-time":"2022-04-28T22:20:06Z","timestamp":1651184406000},"page":"2124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Optimizing Moving Object Trajectories from Roadside Lidar Data by Joint Detection and Tracking"],"prefix":"10.3390","volume":"14","author":[{"given":"Jiaxing","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4922-8228","authenticated-orcid":false,"given":"Wen","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5304-7935","authenticated-orcid":false,"given":"Jon P.","family":"Mills","sequence":"additional","affiliation":[{"name":"School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fay, D., Thakur, G.S., Hui, P., and Helmy, A. (2013, January 5\u20138). Knowledge discovery and causality in urban city traffic: A study using planet scale vehicular imagery data. Proceedings of the 6th ACM SIGSPATIAL International Workshop on Computational Transportation Science, Orlando, FL, USA.","DOI":"10.1145\/2533828.2533836"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.trc.2019.01.007","article-title":"Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors","volume":"100","author":"Zhao","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_3","unstructured":"Wu, J. (2018). Data processing algorithms and applications of LiDAR-enhanced connected infrastructure sensing. [Ph.D. Thesis, University of Nevada]."},{"key":"ref_4","first-page":"18","article-title":"Pollutant emissions from road vehicle in mega city Kolkata, India: Past and present trends","volume":"10","author":"Nagpure","year":"2010","journal-title":"Indian J. Air Pollut. Control"},{"key":"ref_5","unstructured":"Xu, H., Tian, Z., Wu, J., Liu, H., and Zhao, J. (2018). High-Resolution Micro Traffic Data from Roadside LiDAR Sensors for Connected-Vehicles and New Traffic Applications, University of Nevada, Solaris University Transportation Center."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1177\/0361198119844457","article-title":"Vehicle detection and tracking in complex traffic circumstances with roadside LiDAR","volume":"2673","author":"Zhang","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_7","unstructured":"Zhao, J. (2019). Exploring the fundamentals of using infrastructure-based LiDAR sensors to develop connected intersections. [Ph.D. Thesis, Texas Tech University]."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5597","DOI":"10.1109\/JSTARS.2020.3024921","article-title":"Vehicle Tracking and Speed Estimation from Roadside Lidar","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"295","DOI":"10.5194\/isprs-annals-III-3-295-2016","article-title":"Simultaneous Detection and Tracking of Pedestrian from Velodyne Laser Scanning Data","volume":"3","author":"Xiao","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_10","first-page":"32","article-title":"An automatic procedure for vehicle tracking with a roadside LiDAR sensor","volume":"88","author":"Wu","year":"2018","journal-title":"ITE J. Inst. Transp. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"100406","DOI":"10.1109\/ACCESS.2019.2929795","article-title":"Architecture of vehicle trajectories extraction with roadside LiDAR serving connected vehicles","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yan, Z., Duckett, T., and Bellotto, N. (2017, January 24\u201328). Online learning for human classification in 3d lidar-based tracking. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202247"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1177\/0361198119843857","article-title":"Automatic vehicle classification using roadside LiDAR data","volume":"2673","author":"Wu","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, J., Pi, R., Ma, X., Wu, J., Li, H., and Yang, Z. (2021). Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network. Electronics, 10.","DOI":"10.3390\/electronics10070803"},{"key":"ref_15","unstructured":"Wan, E.A., and Van Der Merwe, R. (2000, January 4). The Unscented Kalman Filter for Nonlinear Estimation. Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium, Lake Louise, AB, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MCS.2009.934469","article-title":"The probabilistic data association filter","volume":"29","author":"Daum","year":"2009","journal-title":"IEEE Control Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1520\/JTE20190859","article-title":"Automatic Vehicle Tracking with LiDAR-Enhanced Roadside Infrastructure","volume":"49","author":"Wu","year":"2020","journal-title":"J. Test Eval."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MIS.2019.2918115","article-title":"Automatic vehicle tracking with roadside LiDAR data for the connected-vehicles system","volume":"34","author":"Cui","year":"2019","journal-title":"IEEE Intell. Syst."},{"key":"ref_19","unstructured":"Weng, X., and Kitani, K. (2019). A baseline for 3d multi-object tracking. arXiv."},{"key":"ref_20","unstructured":"Weng, X., Wang, J., Held, D., and Kitani, K. (January, January 24). 3d multi-object tracking: A baseline and new evaluation metrics. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., and Li, H. (2019, January 15\u201320). Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Weng, X., and Kitani, K. (2019, January 27\u201328). Monocular 3d object detection with pseudo-lidar point cloud. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00114"},{"key":"ref_23","unstructured":"Shi, S., Guo, C., Yang, J., and Li, H. (2020). PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection\/3D Tracking\/Domain Adaptation of Waymo Open Dataset Challenges. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Rob. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1002\/nav.3800020109","article-title":"The Hungarian method for the assignment problem","volume":"2","author":"Kuhn","year":"1955","journal-title":"Nav. Res. Logist. Q."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8895696","DOI":"10.1155\/2020\/8895696","article-title":"A 3D Multiobject Tracking Algorithm of Point Cloud Based on Deep Learning","volume":"2020","author":"Wang","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Weng, X., Wang, Y., Man, Y., and Kitani, K.M. (2020, January 13\u201319). Gnn3dmot: Graph neural network for 3d multi-object tracking with 2d-3d multi-feature learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00653"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tong, H., Zhang, H., Meng, H., and Wang, X. (2010, January 25\u201327). Multitarget Tracking Before Detection via Probability Hypothesis Density Filter. Proceedings of the International Conference on Electrical and Control Engineering, Wuhan, China.","DOI":"10.1109\/iCECE.2010.331"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"O\u0161ep, A., Mehner, W., Voigtlaender, P., and Leibe, B. (2018, January 21\u201325). Track, then decide: Category-agnostic vision-based multi-object tracking. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia.","DOI":"10.1109\/ICRA.2018.8460975"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mitzel, D., and Leibe, B. (2012, January 7\u201313). Taking Mobile Multi-Object Tracking to the Next Level: People, Unknown Objects, and Carried Items. Proceedings of the European Conference on Computer Vision, Florence, Italy.","DOI":"10.1007\/978-3-642-33715-4_41"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gonzalez, H., Rodriguez, S. (2019). Track-Before-Detect Framework-Based Vehicle Monocular Vision Sensors. Sensors, 19.","DOI":"10.3390\/s19030560"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, Q.A., and Tsukada, A. (2019, January 9\u201312). Detection-by-Tracking Boosted Online 3D Multi-Object Tracking. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France.","DOI":"10.1109\/IVS.2019.8813856"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4086","DOI":"10.1109\/TITS.2019.2936498","article-title":"Automatic background construction and object detection based on roadside LiDAR","volume":"21","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Shi, S., Guo, C., Jiang, L., Wang, Z., Shi, J., Wang, X., and Li, H. (2020, January 13\u201319). Pv-rcnn: Point-voxel feature set abstraction for 3d object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01054"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.12928\/telkomnika.v14i4.3956","article-title":"SVM parameter optimization using grid search and genetic algorithm to improve classification performance","volume":"14","author":"Syarif","year":"2016","journal-title":"Telkomnika"},{"key":"ref_36","unstructured":"(2021, December 20). SUPERVISELY. Available online: https:\/\/supervise.ly\/."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1177\/0361198118775841","article-title":"Automatic Background Filtering Method for Roadside LiDAR Data","volume":"2672","author":"Wu","year":"2018","journal-title":"Transp. Res. Rec."},{"key":"ref_38","unstructured":"Huang, K., and Hao, Q. (October, January 27). Joint Multi-Object Detection and Tracking with Camera-LiDAR Fusion for Autonomous Driving. Proceedings of the EEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Prague, Czech Republic."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:03:26Z","timestamp":1760137406000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/9\/2124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,28]]},"references-count":38,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["rs14092124"],"URL":"https:\/\/doi.org\/10.3390\/rs14092124","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,28]]}}}