{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:09:35Z","timestamp":1760234975812,"version":"build-2065373602"},"reference-count":97,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T00:00:00Z","timestamp":1626307200000},"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>Crowd size estimation is a challenging problem, especially when the crowd is spread over a significant geographical area. It has applications in monitoring of rallies and demonstrations and in calculating the assistance requirements in humanitarian disasters. Therefore, accomplishing a crowd surveillance system for large crowds constitutes a significant issue. UAV-based techniques are an appealing choice for crowd estimation over a large region, but they present a variety of interesting challenges, such as integrating per-frame estimates through a video without counting individuals twice. Large quantities of annotated training data are required to design, train, and test such a system. In this paper, we have first reviewed several crowd estimation techniques, existing crowd simulators and data sets available for crowd analysis. Later, we have described a simulation system to provide such data, avoiding the need for tedious and error-prone manual annotation. Then, we have evaluated synthetic video from the simulator using various existing single-frame crowd estimation techniques. Our findings show that the simulated data can be used to train and test crowd estimation, thereby providing a suitable platform to develop such techniques. We also propose an automated UAV-based 3D crowd estimation system that can be used for approximately static or slow-moving crowds, such as public events, political rallies, and natural or man-made disasters. We evaluate the results by applying our new framework to a variety of scenarios with varying crowd sizes. The proposed system gives promising results using widely accepted metrics including MAE, RMSE, Precision, Recall, and F1 score to validate the results.<\/jats:p>","DOI":"10.3390\/rs13142780","type":"journal-article","created":{"date-parts":[[2021,7,15]],"date-time":"2021-07-15T09:32:07Z","timestamp":1626341527000},"page":"2780","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Wide Area Multiview Static Crowd Estimation System Using UAV and 3D Training Simulator"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8371-0432","authenticated-orcid":false,"given":"Shivang","family":"Shukla","sequence":"first","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7570-1192","authenticated-orcid":false,"given":"Bernard","family":"Tiddeman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4063-6479","authenticated-orcid":false,"given":"Helen C.","family":"Miles","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,15]]},"reference":[{"key":"ref_1","first-page":"37","article-title":"To count a crowd","volume":"6","author":"Jacobs","year":"1967","journal-title":"Columbia J. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Marsden, M., McGuinness, K., Little, S., and O\u2019Connor, N.E. (September, January 29). ResnetCrowd: A residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078482"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Loy, C.C., Chen, K., Gong, S., and Xiang, T. (2013). Crowd counting and profiling: Methodology and evaluation. Modeling, Simulation and Visual Analysis of Crowds, Springer.","DOI":"10.1007\/978-1-4614-8483-7_14"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1109\/TPAMI.2011.155","article-title":"Pedestrian detection: An evaluation of the state of the art","volume":"34","author":"Dollar","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, Z., Huang, K., and Tan, T. (2008, January 8\u201311). Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection. Proceedings of the IEEE 2008 19th International Conference on Pattern Recognition (ICPR), Tampa, FL, USA.","DOI":"10.1109\/ICPR.2008.4761705"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Arteta, C., Lempitsky, V., and Zisserman, A. (2016). Counting in the wild. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46478-7_30"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ryan, D., Denman, S., Fookes, C., and Sridharan, S. (2009, January 1\u20133). Crowd counting using multiple local features. Proceedings of the IEEE Digital Image Computing: Techniques and Applications (DICTA\u201909), Melbourne, Australia.","DOI":"10.1109\/DICTA.2009.22"},{"key":"ref_8","unstructured":"Ma, R., Li, L., Huang, W., and Tian, Q. (2004, January 1\u20133). On pixel count based crowd density estimation for visual surveillance. Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1109\/TPAMI.2015.2396051","article-title":"Detecting humans in dense crowds using locally-consistent scale prior and global occlusion reasoning","volume":"37","author":"Idrees","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, D., Chen, S., Gao, S., and Ma, Y. (2016, January 27\u201330). Single-image crowd counting via multi-column convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.70"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Chan, A.B. (2020, January 7\u201312). 3d crowd counting via multi-view fusion with 3d gaussian kernels. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6980"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Shi, M., Zhao, X., and Li, L. (2020, January 23\u201328). Active Crowd Counting with Limited Supervision. Proceedings of the Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58565-5_34"},{"key":"ref_13","unstructured":"Wang, M., Cai, H., Han, X., Zhou, J., and Gong, M. (2020). STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting. arXiv."},{"key":"ref_14","unstructured":"Ranjan, V., Wang, B., Shah, M., and Hoai, M. (December, January 30). Uncertainty estimation and sample selection for crowd counting. Proceedings of the Asian Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103092","DOI":"10.1016\/j.autcon.2020.103092","article-title":"Estimation of crowd flow and load on pedestrian bridges using machine learning with sensor fusion","volume":"112","author":"Mustapha","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-020-01132-y","article-title":"Crowd flow estimation from calibrated cameras","volume":"32","author":"Almeida","year":"2021","journal-title":"Mach. Vis. Appl."},{"key":"ref_17","unstructured":"Choi, H., Moon, G., Park, J., and Lee, K.M. (2021). 3DCrowdNet: 2D Human Pose-Guided3D Crowd Human Pose and Shape Estimation in the Wild. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fahad, M.S., and Deepak, A. (2021, January 21\u201323). Crowd Estimation of Real-Life Images with Different View-Points. Proceedings of the International Conference on Innovative Computing and Communications, Delhi, India.","DOI":"10.1007\/978-981-15-5148-2_90"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00138-008-0132-4","article-title":"Crowd analysis: A survey","volume":"19","author":"Zhan","year":"2008","journal-title":"Mach. Vis. Appl."},{"key":"ref_20","first-page":"66","article-title":"Crowd analysis using computer vision techniques","volume":"27","author":"Junior","year":"2010","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_21","first-page":"59","article-title":"A survey of human-sensing: Methods for detecting presence, count, location, track, and identity","volume":"5","author":"Teixeira","year":"2010","journal-title":"ACM Comput. Surv."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2014.01.005","article-title":"Performance evaluation of crowd image analysis using the PETS2009 dataset","volume":"44","author":"Ferryman","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1109\/TCSVT.2014.2358029","article-title":"Crowded scene analysis: A survey","volume":"25","author":"Li","year":"2014","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/TSMCC.2004.829274","article-title":"A survey on visual surveillance of object motion and behaviors","volume":"34","author":"Hu","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part C Appl. Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2014.07.008","article-title":"An evaluation of crowd counting methods, features and regression models","volume":"130","author":"Ryan","year":"2015","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chan, A.B., Liang, Z.S.J., and Vasconcelos, N. (2008, January 23\u201328). Privacy preserving crowd monitoring: Counting people without people models or tracking. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587569"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ferryman, J., and Shahrokni, A. (2009, January 7\u20139). Pets2009: Dataset and challenge. Proceedings of the 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, Snowbird, UT, USA.","DOI":"10.1109\/PETS-WINTER.2009.5399556"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1016\/j.patcog.2010.10.002","article-title":"Semi-supervised elastic net for pedestrian counting","volume":"44","author":"Tan","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_29","first-page":"3","article-title":"Feature mining for localised crowd counting","volume":"1","author":"Chen","year":"2012","journal-title":"BMVC"},{"key":"ref_30","unstructured":"Zhou, B., Wang, X., and Tang, X. (2012, January 16\u201321). Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.engappai.2015.01.007","article-title":"Recent survey on crowd density estimation and counting for visual surveillance","volume":"41","author":"Saleh","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.neucom.2015.12.070","article-title":"Advances and trends in visual crowd analysis: A systematic survey and evaluation of crowd modelling techniques","volume":"186","author":"Zitouni","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3052930","article-title":"Crowd scene understanding from video: A survey","volume":"13","author":"Grant","year":"2017","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl. (TOMM)"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.patrec.2017.07.007","article-title":"A survey of recent advances in cnn-based single image crowd counting and density estimation","volume":"107","author":"Sindagi","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Walach, E., and Wolf, L. (2016, January 11\u201314). Learning to count with cnn boosting. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_41"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Shang, C., Ai, H., and Bai, B. (2016, January 25\u201328). End-to-end crowd counting via joint learning local and global count. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532551"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Onoro-Rubio, D., and L\u00f3pez-Sastre, R.J. (2016, January 11\u201314). Towards perspective-free object counting with deep learning. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_38"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1109\/TCSVT.2018.2837153","article-title":"Beyond counting: Comparisons of density maps for crowd analysis tasks\u2014Counting, detection, and tracking","volume":"29","author":"Kang","year":"2018","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s00371-018-1499-5","article-title":"Convolutional neural networks for crowd behaviour analysis: A survey","volume":"35","author":"Tripathi","year":"2019","journal-title":"Vis. Comput."},{"key":"ref_40","unstructured":"Gao, G., Gao, J., Liu, Q., Wang, Q., and Wang, Y. (2020). Cnn-based density estimation and crowd counting: A survey. arXiv."},{"key":"ref_41","unstructured":"Yan, Z., Yuan, Y., Zuo, W., Tan, X., Wang, Y., Wen, S., and Ding, E. (November, January 27). Perspective-guided convolution networks for crowd counting. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_42","unstructured":"Xiong, H., Lu, H., Liu, C., Liang, L., Cao, Z., and Shen, C. (November, January 27). From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1109\/TIP.2019.2952083","article-title":"Padnet: Pan-density crowd counting","volume":"29","author":"Tian","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kleinmeier, B., Z\u00f6nnchen, B., G\u00f6del, M., and K\u00f6ster, G. (2019). Vadere: An open-source simulation framework to promote interdisciplinary understanding. arXiv.","DOI":"10.17815\/CD.2019.21"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Maury, B., and Faure, S. (2018). Crowds in Equations: An Introduction to the Microscopic Modeling of Crowds, World Scientific.","DOI":"10.1142\/q0163"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.17815\/CD.2016.1","article-title":"Menge: A modular framework for simulating crowd movement","volume":"1","author":"Curtis","year":"2016","journal-title":"Collect. Dyn."},{"key":"ref_47","unstructured":"Wagoum, A.K., Chraibi, M., Zhang, J., and L\u00e4mmel, G. (2015, January 17\u201320). JuPedSim: An open framework for simulating and analyzing the dynamics of pedestrians. Proceedings of the 3rd Conference of Transportation Research Group of India, Kolkata, India."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1126\/science.1116681","article-title":"Pattern-oriented modeling of agent-based complex systems: Lessons from ecology","volume":"310","author":"Grimm","year":"2005","journal-title":"Science"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Van Den Berg, J., Patil, S., Sewall, J., Manocha, D., and Lin, M. (2008, January 15\u201317). Interactive navigation of multiple agents in crowded environments. Proceedings of the 2008 Symposium on Interactive 3D Graphics and Games, Redwood City, CA, USA.","DOI":"10.1145\/1342250.1342272"},{"key":"ref_50","first-page":"1","article-title":"Fire dynamics simulator user\u2019s guide","volume":"1019","author":"McGrattan","year":"2013","journal-title":"NIST Spec. Publ."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Idrees, H., Saleemi, I., Seibert, C., and Shah, M. (2013, January 23\u201328). Multi-source multi-scale counting in extremely dense crowd images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.329"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., and Patel, V.M. (2017, January 22\u201329). Generating high-quality crowd density maps using contextual pyramid cnns. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.206"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, Q., Gao, J., Lin, W., and Li, X. (2020). NWPU-Crowd: A Large-Scale Benchmark for Crowd Counting and Localization. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2020.3013269"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., Yasarla, R., and Patel, V.M. (2020). JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method. IEEE Trans. Pattern Anal. Mach. Intell.","DOI":"10.1109\/TPAMI.2020.3035969"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Idrees, H., Tayyab, M., Athrey, K., Zhang, D., Al-Maadeed, S., Rajpoot, N., and Shah, M. (2018, January 23\u201328). Composition loss for counting, density map estimation and localization in dense crowds. Proceedings of the European Conference on Computer Vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"ref_56","unstructured":"Hu, D., Mou, L., Wang, Q., Gao, J., Hua, Y., Dou, D., and Zhu, X.X. (2020). Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lian, D., Li, J., Zheng, J., Luo, W., and Gao, S. (2019, January 15\u201320). Density Map Regression Guided Detection Network for RGB-D Crowd Counting and Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00192"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Fang, Y., Zhan, B., Cai, W., Gao, S., and Hu, B. (2019). Locality-constrained Spatial Transformer Network for Video Crowd Counting. arXiv.","DOI":"10.1109\/ICME.2019.00145"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wang, Q., Gao, J., Lin, W., and Yuan, Y. (2019, January 16\u201320). Learning from Synthetic Data for Crowd Counting in the Wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00839"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Liu, W., Lis, K.M., Salzmann, M., and Fua, P. (2019, January 3\u20138). Geometric and Physical Constraints for Drone-Based Head Plane Crowd Density Estimation. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967852"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Zhang, Q., and Chan, A.B. (2019, January 15\u201320). Wide-Area Crowd Counting via Ground-Plane Density Maps and Multi-View Fusion CNNs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00849"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Deng, L., Wang, S.H., and Zhang, Y.D. (2020, January 12\u201314). Fully Optimized Convolutional Neural Network Based on Small-Scale Crowd. Proceedings of the 2020 IEEE International Symposium on Circuits and Systems (ISCAS), Seville, Spain.","DOI":"10.1109\/ISCAS45731.2020.9180823"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Mallapuram, S., Ngwum, N., Yuan, F., Lu, C., and Yu, W. (2017, January 24\u201326). Smart city: The state of the art, datasets, and evaluation platforms. Proceedings of the 2017 IEEE\/ACIS 16th International Conference on Computer and Information Science (ICIS), Wuhan, China.","DOI":"10.1109\/ICIS.2017.7960034"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Lim, M.K., Kok, V.J., Loy, C.C., and Chan, C.S. (2014, January 24\u201328). Crowd saliency detection via global similarity structure. Proceedings of the IEEE 2014 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.678"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1109\/TMM.2016.2542585","article-title":"Data-driven crowd understanding: A baseline for a large-scale crowd dataset","volume":"18","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Multimed."},{"key":"ref_66","unstructured":"Bahmanyar, R., Vig, E., and Reinartz, P. (2019). MRCNet: Crowd counting and density map estimation in aerial and ground imagery. arXiv."},{"key":"ref_67","unstructured":"Zhu, P., Wen, L., Du, D., Bian, X., Hu, Q., and Ling, H. (2020). Vision Meets Drones: Past, Present and Future. arXiv."},{"key":"ref_68","unstructured":"Zhu, P., Sun, Y., Wen, L., Feng, Y., and Hu, Q. (2020). Drone Based RGBT Vehicle Detection and Counting: A Challenge. arXiv."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"6661","DOI":"10.1007\/s11042-020-10002-8","article-title":"Multi-scale and multi-column convolutional neural network for crowd density estimation","volume":"80","author":"Chen","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"012025","DOI":"10.1088\/1742-6596\/1828\/1\/012025","article-title":"Crowd Density Estimation Based on Multi-Column Hybrid Convolutional Network","volume":"1828","author":"Guo","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Jingying, W. (2021). A Survey on Crowd Counting Methods and Datasets. Advances in Computer, Communication and Computational Sciences, Springer.","DOI":"10.1007\/978-981-15-4409-5_76"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ma, Y.J., Shuai, H.H., and Cheng, W.H. (2021). Spatiotemporal Dilated Convolution with Uncertain Matching for Video-based Crowd Estimation. IEEE Trans. Multimed.","DOI":"10.1109\/TMM.2021.3050059"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Chen, H., Guo, P., Li, P., Lee, G.H., and Chirikjian, G. (2020). Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-view Geometry. Proceedings of the European Conference on Computer Vision, Glasgow, UK, 23\u201328 August 2020, Springer.","DOI":"10.1007\/978-3-030-58580-8_32"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Benzine, A., Chabot, F., Luvison, B., Pham, Q.C., and Achard, C. (2020, January 13\u201319). Pandanet: Anchor-based single-shot multi-person 3d pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00689"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3392858","article-title":"C-Reference: Improving 2D to 3D Object Pose Estimation Accuracy via Crowdsourced Joint Object Estimation","volume":"4","author":"Song","year":"2020","journal-title":"Proc. ACM Hum.-Comput. Interact."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Chen, H., Guo11, P., Li, P., Lee, G.H., and Chirikjian, G. (2021, January 10). Multi-person 3D Pose Estimation in Crowded Scenes Based on Multi-View Geometry\u2014Supplementary Material. Available online: https:\/\/www.ecva.net\/papers\/eccv_2020\/papers_ECCV\/papers\/123480545.pdf.","DOI":"10.1007\/978-3-030-58580-8_32"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Hashmi, M.F., Ashish, B.K.K., and Keskar, A.G. (2020, January 16\u201318). GAIT analysis: 3D pose estimation and prediction in defence applications using pattern recognition. Proceedings of the Twelfth International Conference on Machine Vision (ICMV 2019), International Society for Optics and Photonics, Amsterdam, The Netherlands.","DOI":"10.1117\/12.2559368"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zheng, C., Zhu, S., Mendieta, M., Yang, T., Chen, C., and Ding, Z. (2021). 3d human pose estimation with spatial and temporal transformers. arXiv.","DOI":"10.1109\/ICCV48922.2021.01145"},{"key":"ref_79","unstructured":"Li, W., Liu, H., Ding, R., Liu, M., and Wang, P. (2021). Lifting Transformer for 3D Human Pose Estimation in Video. arXiv."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.patrec.2021.03.028","article-title":"Animepose: Multi-person 3d pose estimation and animation","volume":"147","author":"Kumarapu","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_81","unstructured":"(2021, January 10). Epic Games; Unreal Engine\/The Most Powerful Real-Time 3D Creation Platform. Available online: https:\/\/www.unrealengine.com."},{"key":"ref_82","unstructured":"Human, M. (2021, February 22). Make Human. Available online: http:\/\/www.makehumancommunity.org\/."},{"key":"ref_83","unstructured":"(2021, March 05). AXYZ; Anima. Available online: https:\/\/secure.axyz-design.com\/."},{"key":"ref_84","unstructured":"Sch\u00f6nberger, J.L., and Frahm, J.M. (2021, April 21). Colmap\/Structure-from-Motion Revisited. Available online: https:\/\/colmap.github.io\/."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Schops, T., Schonberger, J.L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., and Geiger, A. (2017, January 21\u201326). A multi-view stereo benchmark with high-resolution images and multi-camera videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.272"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Stathopoulou, E.K., and Remondino, F. (2019, January 2\u20133). Open-source image-based 3D reconstruction pipelines: Review, comparison and evaluation. Proceedings of the 6th International Workshop LowCost 3D\u2014Sensors, Algorithms, Applications, Strasbourg, France.","DOI":"10.5194\/isprs-archives-XLII-2-W17-331-2019"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Leudet, J., Mikkonen, T., Christophe, F., and M\u00e4nnist\u00f6, T. (2018, January 25\u201327). Virtual Environment for Training Autonomous Vehicles. Proceedings of the Annual Conference Towards Autonomous Robotic Systems, Bristol, UK.","DOI":"10.1007\/978-3-319-96728-8_14"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2487228.2487237","article-title":"Screened poisson surface reconstruction","volume":"32","author":"Kazhdan","year":"2013","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"ref_89","unstructured":"Contributor, Wiki (2021, June 24). Iterative Closest Point. Available online: https:\/\/en.wikipedia.org\/wiki\/Iterative_Closest_Point."},{"key":"ref_90","unstructured":"Marden, S., and Guivant, J. (2012, January 3\u20135). Improving the performance of ICP for real-time applications using an approximate nearest neighbour search. Proceedings of the Australasian Conference on Robotics and Automation, Wellington, New Zealand."},{"key":"ref_91","first-page":"2","article-title":"Fast approximate nearest neighbors with automatic algorithm configuration","volume":"2","author":"Muja","year":"2009","journal-title":"VISAPP (1)"},{"key":"ref_92","unstructured":"Sam, D.B., Peri, S.V., Sundararaman, M.N., Kamath, A., and Radhakrishnan, V.B. (2020). Locate, Size and Count: Accurately Resolving People in Dense Crowds via Detection. IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Sindagi, V.A., and Patel, V.M. (September, January 29). Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. Proceedings of the 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy.","DOI":"10.1109\/AVSS.2017.8078491"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"166856","DOI":"10.1016\/j.ijleo.2021.166856","article-title":"Initial alignment for point cloud registration by improved differential evolution algorithm","volume":"243","author":"Liu","year":"2021","journal-title":"Optik"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"6635446","DOI":"10.1155\/2020\/6635446","article-title":"A Crowd Density Detection Algorithm for Tourist Attractions Based on Monitoring Video Dynamic Information Analysis","volume":"2020","author":"Li","year":"2020","journal-title":"Complexity"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"0018","DOI":"10.5565\/rev\/elcvia.582","article-title":"Autonomous UAV for suspicious action detection using pictorial human pose estimation and classification","volume":"13","author":"Penmetsa","year":"2014","journal-title":"ELCVIA Electron. Lett. Comput. Vis. Image Anal."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s11263-014-0735-3","article-title":"Learning collective crowd behaviors with dynamic pedestrian-agents","volume":"111","author":"Zhou","year":"2015","journal-title":"Int. J. Comput. 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