{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T15:49:49Z","timestamp":1772207389403,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:00:00Z","timestamp":1627344000000},"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>Pedestrian detection and tracking is necessary for autonomous vehicles and traffic management. This paper presents a novel solution to pedestrian detection and tracking for urban scenarios based on Doppler LiDAR that records both the position and velocity of the targets. The workflow consists of two stages. In the detection stage, the input point cloud is first segmented to form clusters, frame by frame. A subsequent multiple pedestrian separation process is introduced to further segment pedestrians close to each other. While a simple speed classifier is capable of extracting most of the moving pedestrians, a supervised machine learning-based classifier is adopted to detect pedestrians with insignificant radial velocity. In the tracking stage, the pedestrian\u2019s state is estimated by a Kalman filter, which uses the speed information to estimate the pedestrian\u2019s dynamics. Based on the similarity between the predicted and detected states of pedestrians, a greedy algorithm is adopted to associate the trajectories with the detection results. The presented detection and tracking methods are tested on two data sets collected in San Francisco, California by a mobile Doppler LiDAR system. The results of the pedestrian detection demonstrate that the proposed two-step classifier can improve the detection performance, particularly for detecting pedestrians far from the sensor. For both data sets, the use of Doppler speed information improves the F1-score and the recall by 15% to 20%. The subsequent tracking from the Kalman filter can achieve 83.9\u201355.3% for the multiple object tracking accuracy (MOTA), where the contribution of the speed measurements is secondary and insignificant.<\/jats:p>","DOI":"10.3390\/rs13152952","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T22:35:02Z","timestamp":1627425302000},"page":"2952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Detection and Tracking of Pedestrians Using Doppler LiDAR"],"prefix":"10.3390","volume":"13","author":[{"given":"Xiaoyi","family":"Peng","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, 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":[[2021,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1109\/TITS.2007.909239","article-title":"A Low-Cost Pedestrian-Detection System with a Single Optical Camera","volume":"9","author":"Cao","year":"2008","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Stewart, R., and Andriluka, M. (2015). End-to-End People Detection in Crowded Scenes. arXiv.","DOI":"10.1109\/CVPR.2016.255"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gaddigoudar, P.K., Balihalli, T.R., Ijantkar, S.S., Iyer, N.C., and Maralappanavar, S. (2017, January 5\u20136). Pedestrian Detection and Tracking Using Particle Filtering. Proceedings of the 2017 International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8229782"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jafari, O.H., Mitzel, D., and Leibe, B. (June, January 31). Real-Time RGB-D Based People Detection and Tracking for Mobile Robots and Head-Worn Cameras. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907688"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Premebida, C., Carreira, J., Batista, J., and Nunes, U. (2014, January 14\u201318). Pedestrian Detection Combining RGB and Dense LIDAR Data. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943141"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.patrec.2014.09.013","article-title":"Detecting and Tracking People in Real Time with RGB-D Camera","volume":"53","author":"Liu","year":"2015","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, C., Yang, B., Song, S., Tian, M., Li, J., Dai, W., and Fang, L. (2018). Calibrate Multiple Consumer RGB-D Cameras for Low-Cost and Efficient 3D Indoor Mapping. Remote. Sens., 10.","DOI":"10.3390\/rs10020328"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Haselich, M., Jobgen, B., Wojke, N., Hedrich, J., and Paulus, D. (2014, January 14\u201318). Confidence-Based Pedestrian Tracking in Unstructured Environments Using 3D Laser Distance Measurements. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA.","DOI":"10.1109\/IROS.2014.6943142"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Cabanes, Q., and Senouci, B. (2017, January 4\u20137). Objects Detection and Recognition in Smart Vehicle Applications: Point Cloud Based Approach. Proceedings of the 2017 Ninth International Conference on Ubiquitous and Future Networks (ICUFN), Milan, Italy.","DOI":"10.1109\/ICUFN.2017.7993795"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, T., Hu, J., Ye, L., and Ding, K. (2021). A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems. Sensors, 21.","DOI":"10.3390\/s21041159"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Pomerleau, F., Krusi, P., Colas, F., Furgale, P., and Siegwart, R. (June, January 31). Long-Term 3D Map Maintenance in Dynamic Environments. Proceedings of the 2014 IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China.","DOI":"10.1109\/ICRA.2014.6907397"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Azim, A., and Aycard, O. (2012, January 3\u20137). Detection, Classification and Tracking of Moving Objects in a 3D Environment. Proceedings of the 2012 IEEE Intelligent Vehicles Symposium, Alcal de Henares, Madrid, Spain.","DOI":"10.1109\/IVS.2012.6232303"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"1516","DOI":"10.1177\/0278364910370216","article-title":"Pedestrian Detection and Tracking Using Three-Dimensional LADAR Data","volume":"29","author":"Mertz","year":"2010","journal-title":"Int. J. Robot. Res."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kidono, K., Miyasaka, T., Watanabe, A., Naito, T., and Miura, J. (2011, January 5\u20139). Pedestrian Recognition Using High-Definition LIDAR. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940433"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zeng Wang, D., and Posner, I. (2015, January 13). Voting for Voting in Online Point Cloud Object Detection. Proceedings of the Robotics: Science and Systems XI, Robotics: Science and Systems Foundation, Rome, Italy.","DOI":"10.15607\/RSS.2015.XI.035"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Navarro, P., Fern\u00e1ndez, C., Borraz, R., and Alonso, D. (2016). A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data. Sensors, 17.","DOI":"10.3390\/s17010018"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yang, Z., Sun, Y., Liu, S., Shen, X., and Jia, J. (2018). IPOD: Intensive Point-Based Object Detector for Point Cloud. arXiv.","DOI":"10.1109\/ICCV.2019.00204"},{"key":"ref_20","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 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Tuzel, O. (2018, January 18\u201322). VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00472"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O. (2019, January 15\u201320). PointPillars: Fast Encoders for Object Detection From Point Clouds. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01298"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1007\/s10514-019-09883-y","article-title":"Online Learning for 3D LiDAR-Based Human Detection: Experimental Analysis of Point Cloud Clustering and Classification Methods","volume":"44","author":"Yan","year":"2020","journal-title":"Auton. Robot."},{"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. Robot. Res."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","unstructured":"Camara, F., Bellotto, N., Cosar, S., Nathanael, D., Althoff, M., Wu, J., Ruenz, J., Dietrich, A., and Fox, C. (2020). Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking. IEEE Trans. Intell. Transport. Syst., 1\u201321.","DOI":"10.1109\/TITS.2020.3006768"},{"key":"ref_27","unstructured":"Zhang, L., Li, Y., and Nevatia, R. (2008, January 23\u201328). Global Data Association for Multi-Object Tracking Using Network Flows. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Brendel, W., Amer, M., and Todorovic, S. (2011, January 20\u201325). Multiobject Tracking as Maximum Weight Independent Set. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995395"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Schulter, S., Vernaza, P., Choi, W., and Chandraker, M. (2017, January 21\u201326). Deep Network Flow for Multi-Object Tracking. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.292"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Weng, X., Wang, J., Held, D., and Kitani, K. (2020). 3D Multi-Object Tracking: A Baseline and New Evaluation Metrics. arXiv.","DOI":"10.1109\/IROS45743.2020.9341164"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103448","DOI":"10.1016\/j.artint.2020.103448","article-title":"Multiple Object Tracking: A Literature Review","volume":"293","author":"Luo","year":"2021","journal-title":"Artif. Intell."},{"key":"ref_32","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":"Robot. Auton. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chiu, H., Prioletti, A., Li, J., and Bohg, J. (2020). Probabilistic 3D Multi-Object Tracking for Autonomous Driving. arXiv.","DOI":"10.1109\/ICRA48506.2021.9561754"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/TITS.2015.2502325","article-title":"Density Enhancement-Based Long-Range Pedestrian Detection Using 3-D Range Data","volume":"17","author":"Li","year":"2016","journal-title":"IEEE Trans. Intell. Transport. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, M., Fu, R., Cheng, W., Wang, L., and Ma, Y. (2019). An Approach to Segment and Track-Based Pedestrian Detection from Four-Layer Laser Scanner Data. Sensors, 19.","DOI":"10.3390\/s19245450"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.1117\/1.1467063","article-title":"Advantages of a New Modulation Scheme in an Optical Self-Mixing Frequency-Modulated Continuous-Wave System","volume":"41","author":"Nordin","year":"2002","journal-title":"Opt. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Royo, S., and Ballesta-Garcia, M. (2019). An Overview of Lidar Imaging Systems for Autonomous Vehicles. Appl. Sci., 9.","DOI":"10.3390\/app9194093"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kim, C., Jung, Y., and Lee, S. (2020). FMCW LiDAR System to Reduce Hardware Complexity and Post-Processing Techniques to Improve Distance Resolution. Sensors, 20.","DOI":"10.3390\/s20226676"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kadlec, E.A., Barber, Z.W., Rupavatharam, K., Angus, E., Galloway, R., Rogers, E.M., Thornton, J., and Crouch, S. (2019, January 7\u201311). Coherent Lidar for Autonomous Vehicle Applications. Proceedings of the 2019 24th OptoElectronics and Communications Conference (OECC) and 2019 International Conference on Photonics in Switching and Computing (PSC), Fukuoka, Japan.","DOI":"10.23919\/PS.2019.8817713"},{"key":"ref_40","unstructured":"Piggott, A.Y. (2020). Understanding the Physics of Coherent LiDAR. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ma, Y., Anderson, J., Crouch, S., and Shan, J. (2019). Moving Object Detection and Tracking with Doppler LiDAR. Remote. Sens., 11.","DOI":"10.3390\/rs11101154"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G. (2016). An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation. Remote. Sens., 8.","DOI":"10.3390\/rs8060501"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inform. Theory"},{"key":"ref_45","unstructured":"Dizaji, F.S. (2019). Lidar Based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods. arXiv."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_47","first-page":"1","article-title":"Moving Object Classification Using 3D Point Cloud in Urban Traffic Environment","volume":"2020","author":"Zhang","year":"2020","journal-title":"J. Adv. Transp."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Lin, Z., Hashimoto, M., Takigawa, K., and Takahashi, K. (2018, January 20\u201322). Vehicle and Pedestrian Recognition Using Multilayer Lidar Based on Support Vector Machine. Proceedings of the 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), Stuttgart, Germany.","DOI":"10.1109\/M2VIP.2018.8600877"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Fan, J., Zhu, X., and Yang, H. (2018, January 1\u20135). Three-Dimensional Real-Time Object Perception Based on a 16-Beam LiDAR for an Autonomous Driving Car. Proceedings of the 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), Kandima, Maldives.","DOI":"10.1109\/RCAR.2018.8621628"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0169-7439(99)00047-7","article-title":"The Mahalanobis Distance","volume":"50","author":"Massart","year":"2000","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Schubert, R., Adam, C., Obst, M., Mattern, N., Leonhardt, V., and Wanielik, G. (2011, January 5\u20139). Empirical Evaluation of Vehicular Models for Ego Motion Estimation. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany.","DOI":"10.1109\/IVS.2011.5940526"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:35:41Z","timestamp":1760164541000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,27]]},"references-count":52,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152952"],"URL":"https:\/\/doi.org\/10.3390\/rs13152952","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,27]]}}}