{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T07:07:13Z","timestamp":1776755233325,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T00:00:00Z","timestamp":1557792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we present a model-free detection-based tracking approach for detecting and tracking moving objects in street scenes from point clouds obtained via a Doppler LiDAR that can not only collect spatial information (e.g., point clouds) but also Doppler images by using Doppler-shifted frequencies. Using our approach, Doppler images are used to detect moving points and determine the number of moving objects followed by complete segmentations via a region growing technique. The tracking approach is based on Multiple Hypothesis Tracking (MHT) with two extensions. One is that a point cloud descriptor, Oriented Ensemble of Shape Function (OESF), is proposed to evaluate the structure similarity when doing object-to-track association. Another is to use Doppler images to improve the estimation of dynamic state of moving objects. The quantitative evaluation of detection and tracking results on different datasets shows the advantages of Doppler LiDAR and the effectiveness of our approach.<\/jats:p>","DOI":"10.3390\/rs11101154","type":"journal-article","created":{"date-parts":[[2019,5,14]],"date-time":"2019-05-14T10:42:33Z","timestamp":1557830553000},"page":"1154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Moving Object Detection and Tracking with Doppler LiDAR"],"prefix":"10.3390","volume":"11","author":[{"given":"Yuchi","family":"Ma","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]},{"given":"John","family":"Anderson","sequence":"additional","affiliation":[{"name":"Geospatial Research Lab, Corbin Field Station, 15319 Magnetic Lane, Woodford, VA 22580, USA"}]},{"given":"Stephen","family":"Crouch","sequence":"additional","affiliation":[{"name":"Blackmore Sensors and Analytics, Inc., Bozeman, MT 59718, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1948-9657","authenticated-orcid":false,"given":"Jie","family":"Shan","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,5,14]]},"reference":[{"key":"ref_1","first-page":"253","article-title":"Object Tracking: A Survey","volume":"7","author":"Pourezzat","year":"2013","journal-title":"Adv. Environ. Biol."},{"key":"ref_2","first-page":"168","article-title":"A Review on Moving Object Detection and Tracking","volume":"5","author":"Prajapati","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.isprsjprs.2010.06.006","article-title":"Automatic detection and tracking of pedestrians from a moving stereo rig","volume":"65","author":"Schindler","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.isprsjprs.2019.02.005","article-title":"Box-level segmentation supervised deep neural networks for accurate and real-time multispectral pedestrian detection","volume":"150","author":"Cao","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","first-page":"103","article-title":"Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels","volume":"54","author":"Luo","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kim, C., Li, F., Ciptadi, A., and Rehg, J.M. (2015, January 7\u201313). Multiple Hypothesis Tracking Revisisted. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.533"},{"key":"ref_7","unstructured":"Spinello, L., Triebel, R., and Siegwart, R. (2008, January 13\u201317). Multimodal People Detection and Tracking in Crowded Scenes. Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, Chicago, IL, USA."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kaestner, R., Maye, J., Pilat, Y., and Siegwart, R. (2012, January 14\u201318). Generative object detection and tracking in 3d range data. Proceedings of the 2012 IEEE International Conference on Robotics and Automation, Saint Paul, MN, USA.","DOI":"10.1109\/ICRA.2012.6224585"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Dewan, A., Caselitz, T., Tipaldi, G.D., and Burgard, W. (2016, January 16\u201321). Motion-based detection and tracking in 3D LiDAR scans. Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden.","DOI":"10.1109\/ICRA.2016.7487649"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.cviu.2006.07.015","article-title":"Laser-based detection and tracking of multiple people in crowds","volume":"106","author":"Cui","year":"2007","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_11","first-page":"175","article-title":"Model Based Vehicle Tracking for Autonomous Driving in Urban Environments","volume":"1","author":"Brock","year":"2009","journal-title":"MIT Press"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.robot.2016.11.014","article-title":"Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle","volume":"88","author":"Wang","year":"2017","journal-title":"Rob. Auton. Syst."},{"key":"ref_13","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 panoramic laser scanning data","volume":"3","author":"Xiao","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1002\/rob.20392","article-title":"Simultaneous egomotion estimation, segmentation, and moving object detection","volume":"28","author":"Yang","year":"2011","journal-title":"J. F. Robot."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2018.05.019","article-title":"Removing non-static objects from 3D laser scan data","volume":"143","author":"Schauer","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cviu.2014.04.011","article-title":"SHOT: Unique signatures of histograms for surface and texture description","volume":"125","author":"Salti","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Moosmann, F., and Stiller, C. (2013, January 6\u201310). Joint self-localization and tracking of generic objects in 3D range data. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630716"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4130","DOI":"10.1109\/TGRS.2016.2537830","article-title":"Automated Detection of Three-Dimensional Cars in Mobile Laser Scanning Point Clouds Using DBM-Hough-Forests","volume":"54","author":"Yu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"31","article-title":"Detection and classification of pole-like objects from mobile laser scanning data of urban environments","volume":"13","author":"Yokoyama","year":"2013","journal-title":"Int. J. Cad\/Cam"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"721","DOI":"10.14358\/PERS.83.10.721","article-title":"LADAR: Frequency-Modulated, Continuous Wave LAser Detection And Ranging","volume":"83","author":"Anderson","year":"2017","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3595","DOI":"10.1364\/AO.28.003595","article-title":"Pulse compression of an FM chirped CO 2 laser","volume":"28","author":"Halmos","year":"1989","journal-title":"Appl. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Slotwinski, A.R., Goodwin, F.E., and Simonson, D.L. (1989). Utilizing GaalAs Laser Diodes As A Source For Frequency Modulated Continuous Wave (FMCW) Coherent Laser Radars. Laser Diode Technology and Applications, International Society for Optics and Photonics.","DOI":"10.1117\/12.976378"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/TAC.1979.1102177","article-title":"An Algorithm for Tracking Multiple Targets","volume":"24","author":"Reid","year":"1979","journal-title":"IEEE Trans. Automat. Contr."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Amditis, A., Thomaidis, G., Maroudis, P., Lytrivis, P., and Karaseitanidis, G. (2012). Multiple Hypothesis Tracking Implementation. Laser Scanner Technol.","DOI":"10.5772\/33583"},{"key":"ref_26","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Kdd, Portland, OR, USA."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.datak.2006.01.013","article-title":"ST-DBSCAN: An algorithm for clustering spatial\u2013temporal data","volume":"60","author":"Birant","year":"2007","journal-title":"Data Knowl. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.isprsjprs.2012.12.002","article-title":"An improved simple morphological filter for the terrain classification of airborne LIDAR data","volume":"77","author":"Pingel","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Blackman, S., and Popoli, R. (1999). Design and Analysis of Modern Tracking Systems (Artech House Radar Library), Artech House."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/JOE.1983.1145560","article-title":"Sonar tracking of multiple targets using joint probabilistic data association","volume":"8","author":"Fortmann","year":"1983","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1145\/1177352.1177355","article-title":"Object tracking: A survey","volume":"38","author":"Yilmaz","year":"2006","journal-title":"Acm Comput. Surv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wohlkinger, W., and Vincze, M. (2011, January 7\u201311). Ensemble of shape functions for 3D object classification. Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics, Phuket, Thailand.","DOI":"10.1109\/ROBIO.2011.6181760"},{"key":"ref_33","unstructured":"Osada, R., Funkhouser, T., Chazelle, B., and Dobkin, D. (2001, January 7\u201311). Matching 3D models with shape distributions. Proceedings of the International Conference on Shape Modeling and Applications, Genova, Italy."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? the kitti vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Papageorgiou, D.J., and Salpukas, M.R. (2009). The maximum weight independent set problem for data association in multiple hypothesis tracking. Optimization and Cooperative Control Strategies, Springer.","DOI":"10.1007\/978-3-540-88063-9_15"},{"key":"ref_36","first-page":"424","article-title":"A new algorithm for the maximum-weight clique problem","volume":"8","year":"2001","journal-title":"Nord. J. Comput."},{"key":"ref_37","unstructured":"Milan, A., Leal-Taix\u00e9, L., Reid, I., Roth, S., and Schindler, K. (2016). MOT16: A benchmark for multi-object tracking. arXiv."},{"key":"ref_38","unstructured":"Stiefelhagen, R., Bernardin, K., Bowers, R., Garofolo, J., Mostefa, D., and Soundararajan, P. (2006). The CLEAR 2006 evaluation. Proceedings of the International Evaluation Workshop on Classification of Events, Activities and Relationships, Springer."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1154\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:51:56Z","timestamp":1760187116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/10\/1154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,14]]},"references-count":38,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["rs11101154"],"URL":"https:\/\/doi.org\/10.3390\/rs11101154","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,14]]}}}