{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T15:19:19Z","timestamp":1760800759318,"version":"build-2065373602"},"reference-count":104,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T00:00:00Z","timestamp":1726617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["BCS-2149229"],"award-info":[{"award-number":["BCS-2149229"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as \u201cflickering\u201d in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.<\/jats:p>","DOI":"10.3390\/rs16183453","type":"journal-article","created":{"date-parts":[[2024,9,18]],"date-time":"2024-09-18T09:49:19Z","timestamp":1726652959000},"page":"3453","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0911-8052","authenticated-orcid":false,"given":"Shashank","family":"Karki","sequence":"first","affiliation":[{"name":"Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA"}]},{"given":"Thomas J.","family":"Pingel","sequence":"additional","affiliation":[{"name":"Department of Geography, Binghamton University, Binghamton, NY 13902, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1449-2571","authenticated-orcid":false,"given":"Timothy D.","family":"Baird","sequence":"additional","affiliation":[{"name":"Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA"}]},{"given":"Addison","family":"Flack","sequence":"additional","affiliation":[{"name":"Department of Geography, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2940-3594","authenticated-orcid":false,"given":"Todd","family":"Ogle","sequence":"additional","affiliation":[{"name":"University Libraries, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1177\/0309132510376851","article-title":"The Great Indoors: Research Frontiers on Indoor Environments as Active Political-Ecological Spaces","volume":"35","author":"Simon","year":"2011","journal-title":"Prog. Hum. Geogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1038\/sj.jea.7500165","article-title":"The National Human Activity Pattern Survey (NHAPS): A Resource for Assessing Exposure to Environmental Pollutants","volume":"11","author":"Klepeis","year":"2001","journal-title":"J. Expo Sci. Environ. Epidemiol."},{"doi-asserted-by":"crossref","unstructured":"Odonohue, D. (2024, April 14). Everything You Need To Know About Indoor Navigation And Mapping\u2014April 14, 2024. Available online: https:\/\/mapscaping.com\/indoor-navigation-and-mapping\/.","key":"ref_3","DOI":"10.7748\/phc.34.4.14.s6"},{"unstructured":"Sabins, F.F., and Ellis, J.M. (2020). Remote Sensing: Principles, Interpretation, and Applications, Waveland Press. [4th ed.].","key":"ref_4"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103399","DOI":"10.1016\/j.autcon.2020.103399","article-title":"Mobile Indoor Mapping Technologies: A Review","volume":"120","author":"Otero","year":"2020","journal-title":"Autom. Constr."},{"unstructured":"Schowengerdt, R.A. (2006). Remote Sensing: Models and Methods for Image Processing, Elsevier.","key":"ref_6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cosrev.2017.03.002","article-title":"Indoor Location Based Services Challenges, Requirements and Usability of Current Solutions","volume":"24","author":"Basiri","year":"2017","journal-title":"Comput. Sci. Rev."},{"doi-asserted-by":"crossref","unstructured":"Dwiyasa, F., and Lim, M.-H. (2016, January 4\u20137). A Survey of Problems and Approaches in Wireless-Based Indoor Positioning. Proceedings of the 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Alcala de Henares, Spain.","key":"ref_8","DOI":"10.1109\/IPIN.2016.7743591"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.isprsjprs.2021.05.006","article-title":"Crowdsourcing-Based Indoor Mapping Using Smartphones: A Survey","volume":"177","author":"Zhou","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1061\/(ASCE)0733-9453(2002)128:3(79)","article-title":"Surveying and Mapping: History, Current Status, and Future Projections","volume":"128","author":"Wolf","year":"2002","journal-title":"J. Surv. Eng."},{"key":"ref_11","first-page":"4688","article-title":"A Generalized Single-Channel Method for Retrieving Land Surface Temperature from Remote Sensing Data","volume":"108","author":"Sobrino","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1002\/met.287","article-title":"Remote Sensing Land Surface Temperature for Meteorology and Climatology: A Review","volume":"18","author":"Tomlinson","year":"2011","journal-title":"Meteorol. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1080\/01431169008955102","article-title":"Remote Sensing of Weather Impacts on Vegetation in Non-Homogeneous Areas","volume":"11","author":"Kogan","year":"1990","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/jpe\/rtm005","article-title":"Remote Sensing Imagery in Vegetation Mapping: A Review","volume":"1","author":"Xie","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"150","DOI":"10.2471\/BLT.11.088302","article-title":"Reduced Death Rates from Cyclones in Bangladesh: What More Needs to Be Done?","volume":"90","author":"Haque","year":"2012","journal-title":"Bull. World Health Organ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2011.06.023","article-title":"Enhancing Temporal Resolution of Satellite Imagery for Public Health Studies: A Case Study of West Nile Virus Outbreak in Los Angeles in 2007","volume":"117","author":"Liu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0065-308X(00)47011-9","article-title":"The Potential of Geographical Information Systems and Remote Sensing in the Epidemiology and Control of Human Helminth Infections","volume":"Volume 47","author":"Brooker","year":"2000","journal-title":"Advances in Parasitology"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0065-308X(00)47005-3","article-title":"An Overview of Remote Sensing and Geodesy for Epidemiology and Public Health Application","volume":"Volume 47","author":"Hay","year":"2000","journal-title":"Advances in Parasitology"},{"unstructured":"Harris, R. (1987). Satellite Remote Sensing. An Introduction, Routledge and Kegan Paul.","key":"ref_19"},{"doi-asserted-by":"crossref","unstructured":"Xue, J., and Su, B. (2022, November 29). Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. Available online: https:\/\/www.hindawi.com\/journals\/js\/2017\/1353691\/.","key":"ref_20","DOI":"10.1155\/2017\/1353691"},{"doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.-A., and Rahman, A. (2020). Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review. Remote Sens., 12.","key":"ref_21","DOI":"10.3390\/rs12071135"},{"doi-asserted-by":"crossref","unstructured":"Alqurashi, A.F., Kumar, L., and Sinha, P. (2016). Urban Land Cover Change Modelling Using Time-Series Satellite Images: A Case Study of Urban Growth in Five Cities of Saudi Arabia. Remote Sens., 8.","key":"ref_22","DOI":"10.3390\/rs8100838"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.eswa.2009.05.062","article-title":"Improving Location Awareness in Indoor Spaces Using RFID Technology","volume":"37","author":"Tesoriero","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/014311600210092","article-title":"Land Cover Mapping of Large Areas from Satellites: Status and Research Priorities","volume":"21","author":"Cihlar","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"8387","DOI":"10.1080\/01431161.2018.1550919","article-title":"The Development of Remote Sensing in the Last 40 Years","volume":"39","author":"Cracknell","year":"2018","journal-title":"Int. J. Remote Sens."},{"unstructured":"Peterson, B., Bruckner, D., and Heye, S. (1997, January 16\u201319). Measuring GPS Signals Indoors. Proceedings of the Institute of Navigation ION GPS-97, Kansas City, MI, USA.","key":"ref_26"},{"key":"ref_27","first-page":"121","article-title":"A Survey of Indoor Positioning and Object Locating Systems","volume":"10","author":"Koyuncu","year":"2010","journal-title":"Int. J. Comput. Sci. Netw. Secur. (IJCSNS)"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1002\/esp.3366","article-title":"Topographic Structure from Motion: A New Development in Photogrammetric Measurement","volume":"38","author":"Fonstad","year":"2013","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1177\/0309133308089496","article-title":"Airborne LiDAR for DEM Generation: Some Critical Issues","volume":"32","author":"Liu","year":"2008","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"unstructured":"Chen, J. (2018, January 15\u201317). Grid Referencing of Buildings. Proceedings of the Adjunct Proceedings of the 14th International Conference on Location Based Services, Zurich, Switzerland.","key":"ref_30"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1111\/cgf.14021","article-title":"State-of-the-Art in Automatic 3D Reconstruction of Structured Indoor Environments","volume":"39","author":"Pintore","year":"2020","journal-title":"Comput. Graph. Forum"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2139","DOI":"10.1109\/TITS.2019.2915087","article-title":"Placement Optimization of Multiple Lidar Sensors for Autonomous Vehicles","volume":"21","author":"Kim","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Zlatanova, S., Sithole, G., Nakagawa, M., and Zhu, Q. (2013, January 11\u201313). Problems In Indoor Mapping and Modelling. Proceedings of the ISPRS Acquisition and Modelling of Indoor and Enclosed Environments 2013, Cape Town, South Arfica. Volume XL-4-W4.","key":"ref_33","DOI":"10.5194\/isprsarchives-XL-4-W4-63-2013"},{"doi-asserted-by":"crossref","unstructured":"Flor\u00e9en, P., Kr\u00fcger, A., and Spasojevic, M. (2010). Indoor Positioning Using GPS Revisited. Pervasive Computing, Springer.","key":"ref_34","DOI":"10.1007\/978-3-642-12654-3"},{"unstructured":"Ijaz, F., Yang, H., Ahmad, A., and Lee, C. (2013, January 27\u201330). Indoor Positioning: A Review of Indoor Ultrasonic Positioning Systems. Proceedings of the Advanced Communication Technology (ICACT), 2013 15th International Conference, PyeongChang, Republic of Korea.","key":"ref_35"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3846\/1392-1541.2009.35.18-22","article-title":"Overview of Current Indoor Positioning Systems","volume":"35","author":"Mautz","year":"2009","journal-title":"Geod. Ir Kartogr."},{"doi-asserted-by":"crossref","unstructured":"Li, K.-J. (2008). Indoor Space: A New Notion of Space. Web and Wireless Geographic Information System, Springer.","key":"ref_37","DOI":"10.1007\/978-3-540-89903-7_1"},{"doi-asserted-by":"crossref","unstructured":"Montello, D.R. (2024, May 16). You Are Where? The Function and Frustration of You-Are-Here (YAH) Maps: Spatial Cognition & Computation; Volume 10, pp 2\u20133. Available online: https:\/\/www.tandfonline.com\/doi\/abs\/10.1080\/13875860903585323.","key":"ref_38","DOI":"10.1080\/13875860903585323"},{"key":"ref_39","first-page":"1","article-title":"Indoor Cartography","volume":"47","author":"Chen","year":"2019","journal-title":"Cartogr. Geogr. Inf. Sci."},{"doi-asserted-by":"crossref","unstructured":"Giudice, N.A., Walton, L.A., and Worboys, M. (2010, January 2). The Informatics of Indoor and Outdoor Space: A Research Agenda. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness, San Jose, CA, USA.","key":"ref_40","DOI":"10.1145\/1865885.1865897"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/0010-4485(90)90071-J","article-title":"The Difference between CAD and GIS","volume":"22","author":"Newell","year":"1990","journal-title":"Comput.-Aided Des."},{"doi-asserted-by":"crossref","unstructured":"Sulaiman, M.Z., Aziz, M.N.A., Bakar, M.H.A., Halili, N.A., and Azuddin, M.A. (2020). Matterport: Virtual Tour as A New Marketing Approach in Real Estate Business During Pandemic COVID-19, Atlantis Press.","key":"ref_42","DOI":"10.2991\/assehr.k.201202.079"},{"key":"ref_43","first-page":"1059","article-title":"Capability of Matterport 3d Camera for Industrial Archaeology Sites Inventory","volume":"42","author":"Shults","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1177\/2399808318796416","article-title":"Digital Twins","volume":"45","author":"Batty","year":"2018","journal-title":"Environ. Plan. B Urban Anal. City Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.cirpj.2020.02.002","article-title":"Characterising the Digital Twin: A Systematic Literature Review","volume":"29","author":"Jones","year":"2020","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"100359","DOI":"10.1016\/j.patter.2021.100359","article-title":"Digital Twins of the Natural Environment","volume":"2","author":"Blair","year":"2021","journal-title":"Patterns"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.inffus.2018.11.002","article-title":"Data Fusion in Cyber-Physical-Social Systems: State-of-the-Art and Perspectives","volume":"51","author":"Wang","year":"2019","journal-title":"Inf. Fusion"},{"doi-asserted-by":"crossref","unstructured":"Schluse, M., and Rossmann, J. (2016, January 3\u20135). From Simulation to Experimentable Digital Twins: Simulation-Based Development and Operation of Complex Technical Systems. Proceedings of the 2016 IEEE International Symposium on Systems Engineering (ISSE), Edinburgh, UK.","key":"ref_48","DOI":"10.1109\/SysEng.2016.7753162"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s43020-021-00041-3","article-title":"Indoor Navigation: State of the Art and Future Trends","volume":"2","author":"Li","year":"2021","journal-title":"Satell. Navig."},{"unstructured":"Purohit, A., Sun, Z., Mokaya, F., and Zhang, P. (2011, January 12\u201314). SensorFly: Controlled-Mobile Sensing Platform for Indoor Emergency Response Applications. Proceedings of the 10th ACM\/IEEE International Conference on Information Processing in Sensor Networks, Chicago, IL, USA.","key":"ref_50"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.buildenv.2015.02.036","article-title":"A New 3D Indoor\/Outdoor Spatial Model for Indoor Emergency Response Facilitation","volume":"89","author":"Tashakkori","year":"2015","journal-title":"Build. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"289","DOI":"10.5194\/isprs-archives-XLI-B4-289-2016","article-title":"A Review of Recent Research in Indoor Modelling & Mapping","volume":"XLI-B4","author":"Gunduz","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"102915","DOI":"10.1016\/j.autcon.2019.102915","article-title":"A Vision and Learning-Based Indoor Localization and Semantic Mapping Framework for Facility Operations and Management","volume":"107","author":"Wei","year":"2019","journal-title":"Autom. Constr."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"102592","DOI":"10.1016\/j.scs.2020.102592","article-title":"How Design Shapes Space Choice Behaviors in Public Urban and Shared Indoor Spaces\u2014A Review","volume":"65","author":"Jens","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.amepre.2004.10.025","article-title":"Influences of Building Design and Site Design on Physical Activity: Research and Intervention Opportunities","volume":"28","author":"Zimring","year":"2005","journal-title":"Am. J. Prev. Med."},{"unstructured":"Hillier, B., Leaman, A., Stansall, P., and Bedford, M. (2022, December 15). Space Syntax. Available online: https:\/\/journals.sagepub.com\/doi\/abs\/10.1068\/b030147?casa_token=uXzG9WNvYzgAAAAA:ERkqLR5WTkPhvr6x7eJdFTkX9kpy-_ylZ5qbaReN_oNI_ak2juuD9OshMTg8VycVWj5xc_JLbsOK.","key":"ref_56"},{"doi-asserted-by":"crossref","unstructured":"Petrovska, N., and Stevanovic, A. (2015, January 15\u201318). Traffic Congestion Analysis Visualisation Tool. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Gran Canaria, Spain.","key":"ref_57","DOI":"10.1109\/ITSC.2015.243"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1177\/0044118X20916617","article-title":"School Surveillance in Context: High School Students\u2019 Perspectives on CCTV, Privacy, and Security","volume":"52","author":"Birnhack","year":"2020","journal-title":"Youth Soc."},{"unstructured":"Menegatti, E., Michael, N., Berns, K., and Yamaguchi, H. (2016). RGB-D Human Detection and Tracking for Industrial Environments. Intelligent Autonomous Systems 13, Springer International Publishing.","key":"ref_59"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.forsciint.2010.04.021","article-title":"Tracking People and Cars Using 3D Modeling and CCTV","volume":"202","author":"Edelman","year":"2010","journal-title":"Forensic Sci. Int."},{"unstructured":"(2024, September 08). Video Surveillance and Public Space: Surveillance Society vs. Security State|SpringerLink. Available online: https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-11756-5_14.","key":"ref_61"},{"unstructured":"(2024, September 08). A Detailed Comparison of LiDAR, Radar and Camera Technology. Available online: https:\/\/insights.outsight.ai\/how-does-lidar-compares-to-cameras-and-radars\/.","key":"ref_62"},{"doi-asserted-by":"crossref","unstructured":"G\u00fcnter, A., B\u00f6ker, S., K\u00f6nig, M., and Hoffmann, M. (2020, January 20\u201323). Privacy-Preserving People Detection Enabled by Solid State LiDAR. Proceedings of the 2020 16th International Conference on Intelligent Environments (IE), Madrid, Spain.","key":"ref_63","DOI":"10.1109\/IE49459.2020.9154970"},{"doi-asserted-by":"crossref","unstructured":"Nielsen, M.S., Nikolov, I., Kruse, E.K., Garn\u00e6s, J., and Madsen, C.B. (2023). Quantifying the Influence of Surface Texture and Shape on Structure from Motion 3D Reconstructions. Sensors, 23.","key":"ref_64","DOI":"10.3390\/s23010178"},{"doi-asserted-by":"crossref","unstructured":"Kang, Z., Yang, J., Yang, Z., and Cheng, S. (2020). A Review of Techniques for 3D Reconstruction of Indoor Environments. ISPRS Int. J. Geo-Inf., 9.","key":"ref_65","DOI":"10.3390\/ijgi9050330"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"105","DOI":"10.20517\/ir.2021.20","article-title":"Deep Learning for LiDAR-Only and LiDAR-Fusion 3D Perception: A Survey","volume":"2","author":"Wu","year":"2022","journal-title":"Intell. Robot."},{"doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 12\u201316). LOAM: Lidar Odometry and Mapping in Real-Time. Proceedings of the Robotics: Science and Systems X, Berkeley, CA, USA.","key":"ref_67","DOI":"10.15607\/RSS.2014.X.007"},{"doi-asserted-by":"crossref","unstructured":"Madani, K., Peaucelle, D., and Gusikhin, O. (2018). Fusing LiDAR and Radar Data to Perform SLAM in Harsh Environments. Informatics in Control, Automation and Robotics: 13th International Conference, ICINCO 2016, Lisbon, Portugal, 29\u201331 July 2016, Springer International Publishing.","key":"ref_68","DOI":"10.1007\/978-3-319-55011-4"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/MITS.2022.3162886","article-title":"Comparative Analysis of Radar and Lidar Technologies for Automotive Applications","volume":"15","author":"Bilik","year":"2023","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"doi-asserted-by":"crossref","unstructured":"Mielle, M., Magnusson, M., and Lilienthal, A.J. (2019, January 4\u20136). A Comparative Analysis of Radar and Lidar Sensing for Localization and Mapping. Proceedings of the 2019 European Conference on Mobile Robots (ECMR), Prague, Czech Republic.","key":"ref_70","DOI":"10.1109\/ECMR.2019.8870345"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1120","DOI":"10.1890\/10-0274.1","article-title":"Using Lidar and Radar Measurements to Constrain Predictions of Forest Ecosystem Structure and Function","volume":"21","author":"Antonarakis","year":"2011","journal-title":"Ecol. Appl."},{"unstructured":"Zou, Z., Shi, Z., Guo, Y., and Ye, J. (2019). Object Detection in 20 Years: A Survey. arXiv.","key":"ref_72"},{"key":"ref_73","first-page":"1","article-title":"Deep Learning for Computer Vision: A Brief Review","volume":"2018","author":"Voulodimos","year":"2018","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1186\/1687-6180-2013-176","article-title":"Human Detection in Surveillance Videos and Its Applications\u2014A Review","volume":"2013","author":"Paul","year":"2013","journal-title":"EURASIP J. Adv. Signal Process."},{"unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Proceedings of the Advances in Neural Information Processing Systems, Montreal, Canada.","key":"ref_75"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","key":"ref_76","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"7853","DOI":"10.1007\/s00521-022-08077-5","article-title":"Improved YOLOv5 Network for Real-Time Multi-Scale Traffic Sign Detection","volume":"35","author":"Wang","year":"2023","journal-title":"Neural Comput. Applic"},{"doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","key":"ref_78","DOI":"10.1109\/CVPR.2017.690"},{"unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv.","key":"ref_79"},{"unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv.","key":"ref_80"},{"doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2022). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. arXiv.","key":"ref_81","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"doi-asserted-by":"crossref","unstructured":"Molchanov, V.V., Vishnyakov, B.V., Vizilter, Y.V., Vishnyakova, O.V., and Knyaz, V.A. (2017, January 29). Pedestrian Detection in Video Surveillance Using Fully Convolutional YOLO Neural Network. Proceedings of the Automated Visual Inspection and Machine Vision II, SPIE, Munich, Germany.","key":"ref_83","DOI":"10.1117\/12.2270326"},{"doi-asserted-by":"crossref","unstructured":"Garg, R., and Singh, S. (2021, January 3\u20134). Intelligent Video Surveillance Based on YOLO: A Comparative Study. Proceedings of the 2021 International Conference on Advances in Computing, Communication, and Control (ICAC3), Mumbai, India.","key":"ref_84","DOI":"10.1109\/ICAC353642.2021.9697321"},{"doi-asserted-by":"crossref","unstructured":"Nguyen, H.H., Ta, T.N., Nguyen, N.C., Bui, V.T., Pham, H.M., and Nguyen, D.M. (2021, January 13\u201315). YOLO Based Real-Time Human Detection for Smart Video Surveillance at the Edge. Proceedings of the 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam.","key":"ref_85","DOI":"10.1109\/ICCE48956.2021.9352144"},{"doi-asserted-by":"crossref","unstructured":"Kannadaguli, P. (2020, January 8\u20139). YOLO v4 Based Human Detection System Using Aerial Thermal Imaging for UAV Based Surveillance Applications. Proceedings of the 2020 International Conference on Decision Aid Sciences and Application (DASA), Sakheer, Bahrain.","key":"ref_86","DOI":"10.1109\/DASA51403.2020.9317198"},{"doi-asserted-by":"crossref","unstructured":"Sualeh, M., and Kim, G.-W. (2019). Dynamic Multi-LiDAR Based Multiple Object Detection and Tracking. Sensors, 19.","key":"ref_87","DOI":"10.3390\/s19061474"},{"unstructured":"BenAbdelkader, C., Cutler, R., and Davis, L. (2002, January 20\u201321). Motion-Based Recognition of People in EigenGait Space. Proceedings of the Fifth IEEE International Conference on Automatic Face Gesture Recognition, Washinton, DC, USA.","key":"ref_88"},{"doi-asserted-by":"crossref","unstructured":"Villarreal, M., Baird, T.D., Tarazaga, P.A., Kniola, D.J., Pingel, T.J., and Sarlo, R. (2024). Shared Space and Resource Use within a Building Environment: An Indoor Geography. Geogr. J., e12604.","key":"ref_89","DOI":"10.1111\/geoj.12604"},{"doi-asserted-by":"crossref","unstructured":"Chan, T.H., Hesse, H., and Ho, S.G. (2021, January 23\u201326). LiDAR-Based 3D SLAM for Indoor Mapping. Proceedings of the 2021 7th International Conference on Control, Automation and Robotics (ICCAR), Singapore.","key":"ref_90","DOI":"10.1109\/ICCAR52225.2021.9463503"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"7073","DOI":"10.1109\/LRA.2021.3092274","article-title":"LiDAR SLAM With Plane Adjustment for Indoor Environment","volume":"6","author":"Zhou","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"92394","DOI":"10.1109\/ACCESS.2021.3092687","article-title":"Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey","volume":"9","author":"Sharif","year":"2021","journal-title":"IEEE Access"},{"doi-asserted-by":"crossref","unstructured":"Szeliski, R. (2022). Computer Vision: Algorithms and Applications, Springer Nature.","key":"ref_93","DOI":"10.1007\/978-3-030-34372-9"},{"unstructured":"Culjak, I., Abram, D., Pribanic, T., Dzapo, H., and Cifrek, M. (2012, January 21\u201325). A Brief Introduction to OpenCV. Proceedings of the 2012 Proceedings of the 35th International Convention MIPRO, Opatija, Croatia.","key":"ref_94"},{"doi-asserted-by":"crossref","unstructured":"Schulte-Tigges, J., F\u00f6rster, M., Nikolovski, G., Reke, M., Ferrein, A., Kaszner, D., Matheis, D., and Walter, T. (2022). Benchmarking of Various LiDAR Sensors for Use in Self-Driving Vehicles in Real-World Environments. Sensors, 22.","key":"ref_95","DOI":"10.3390\/s22197146"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Make"},{"doi-asserted-by":"crossref","unstructured":"Remagnino, P., Jones, G.A., Paragios, N., and Regazzoni, C.S. (2002). An Improved Adaptive Background Mixture Model for Real-Time Tracking with Shadow Detection. Video-Based Surveillance Systems: Computer Vision and Distributed Processing, Springer.","key":"ref_97","DOI":"10.1007\/978-1-4615-0913-4"},{"key":"ref_98","first-page":"120","article-title":"The OpenCV Library","volume":"25","author":"Bradski","year":"2000","journal-title":"Dr. Dobb\u2019s J."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/34.1000236","article-title":"Mean Shift: A Robust Approach toward Feature Space Analysis","volume":"24","author":"Comaniciu","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"6844","DOI":"10.1007\/s10489-022-03930-5","article-title":"Semantic Segmentation of 3D LiDAR Data Using Deep Learning: A Review of Projection-Based Methods","volume":"53","author":"Jhaldiyal","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.cag.2021.07.003","article-title":"A Comprehensive Survey of LIDAR-Based 3D Object Detection Methods","volume":"99","author":"Zamanakos","year":"2021","journal-title":"Comput. Graph."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"7699","DOI":"10.1109\/TITS.2020.3007631","article-title":"Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods","volume":"22","author":"Chen","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"doi-asserted-by":"crossref","unstructured":"Elaksher, A., Ali, T., and Alharthy, A. (2022). A Quantitative Assessment of LIDAR Data Accuracy|EndNote Click. Remote Sens., 15.","key":"ref_103","DOI":"10.3390\/rs15020442"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"371","DOI":"10.5194\/isprs-archives-XLIII-B1-2020-371-2020","article-title":"Accuracy assessment and calibration of low-cost autonomous lidar sensors","volume":"XLIII-B1-2020","author":"Glennie","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3453\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:58:35Z","timestamp":1760111915000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/18\/3453"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,18]]},"references-count":104,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16183453"],"URL":"https:\/\/doi.org\/10.3390\/rs16183453","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,9,18]]}}}