{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T02:21:59Z","timestamp":1783131719952,"version":"3.54.6"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376252"],"award-info":[{"award-number":["62376252"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LZ22F030003"],"award-info":[{"award-number":["LZ22F030003"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Displays"],"published-print":{"date-parts":[[2026,9]]},"DOI":"10.1016\/j.displa.2026.103493","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T05:35:26Z","timestamp":1777527326000},"page":"103493","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A multi-task visual framework: Geometry-guided UAV crowd counting and localization for media practice"],"prefix":"10.1016","volume":"94","author":[{"given":"Ziqing","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longfei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6704-0301","authenticated-orcid":false,"given":"Huiying","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinzhong","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wouladje","family":"Cabrel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Golden Tendekai","family":"Mumanikidzwa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.displa.2026.103493_b1","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":"10.1016\/j.displa.2026.103493_b2","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1109\/TPAMI.2020.3013269","article-title":"NWPU-crowd: A large-scale benchmark for crowd counting and localization","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103493_b3","first-page":"331","article-title":"Clarifying journalism\u2019s quantitative turn: A typology for evaluating data journalism, computational journalism, and computer-assisted reporting","volume":"3","author":"Coddington","year":"2015","journal-title":"Digit. Journal."},{"key":"10.1016\/j.displa.2026.103493_b4","first-page":"157","article-title":"The promise of computational journalism","volume":"6","author":"Flew","year":"2012","journal-title":"Journal. Pr."},{"key":"10.1016\/j.displa.2026.103493_b5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"10.1016\/j.displa.2026.103493_b6","doi-asserted-by":"crossref","first-page":"48572","DOI":"10.1109\/ACCESS.2019.2909530","article-title":"Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges","volume":"7","author":"Shakhatreh","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.displa.2026.103493_b7","doi-asserted-by":"crossref","first-page":"7380","DOI":"10.1109\/TPAMI.2021.3119563","article-title":"Detection and tracking meet drones challenge","volume":"44","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103493_b8","doi-asserted-by":"crossref","first-page":"516","DOI":"10.3390\/rs13030516","article-title":"Vision transformers for remote sensing image classification","volume":"13","author":"Bazi","year":"2021","journal-title":"Remote. Sens."},{"key":"10.1016\/j.displa.2026.103493_b9","doi-asserted-by":"crossref","unstructured":"Y. Zhang, D. Zhou, S. Chen, S. Gao, Y. Ma, Single-Image Crowd Counting via Multi-Column Convolutional Neural Network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2016, pp. 589\u2013597.","DOI":"10.1109\/CVPR.2016.70"},{"key":"10.1016\/j.displa.2026.103493_b10","doi-asserted-by":"crossref","unstructured":"Y. Li, X. Zhang, D. Chen, CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 1091\u20131100.","DOI":"10.1109\/CVPR.2018.00120"},{"key":"10.1016\/j.displa.2026.103493_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.cviu.2020.102907","article-title":"UA-DETRAC: A new benchmark and protocol for multi-object detection and tracking","volume":"193","author":"Wen","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"10.1016\/j.displa.2026.103493_b12","doi-asserted-by":"crossref","unstructured":"D. Du, Y. Qi, H. Yu, Y. Yang, K. Duan, G. Li, W. Zhang, Q. Huang, Q. Tian, The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 370\u2013386.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"10.1016\/j.displa.2026.103493_b13","doi-asserted-by":"crossref","unstructured":"Q. Song, C. Wang, Z. Jiang, Y. Wang, Y. Tai, C. Wang, J. Li, F. Huang, Y. Wu, Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2021, pp. 3365\u20133374.","DOI":"10.1109\/ICCV48922.2021.00335"},{"key":"10.1016\/j.displa.2026.103493_b14","doi-asserted-by":"crossref","first-page":"6040","DOI":"10.1109\/TMM.2022.3203870","article-title":"Focal inverse distance transform maps for crowd localization","volume":"25","author":"Liang","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"10.1016\/j.displa.2026.103493_b15","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1177\/1461444812463345","article-title":"Data-driven journalism and the public good: \u201ccomputer-assisted-reporters\u201d and \u201cprogrammer-journalists\u201d in Chicago","volume":"15","author":"Parasie","year":"2012","journal-title":"New Media Soc."},{"key":"10.1016\/j.displa.2026.103493_b16","first-page":"815","article-title":"SHARE, LIKE, RECOMMEND decoding the social media news consumer","volume":"13","author":"Hermida","year":"2012","journal-title":"Journal. Stud."},{"key":"10.1016\/j.displa.2026.103493_b17","first-page":"323","article-title":"Emerging journalistic verification practices concerning social media","volume":"10","author":"Brandtzaeg","year":"2015","journal-title":"Journal. Pr."},{"key":"10.1016\/j.displa.2026.103493_b18","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1080\/10580530.2012.716740","article-title":"Benefits, adoption barriers and myths of open data and open government","volume":"29","author":"Janssen","year":"2012","journal-title":"Inf. Syst. Manage."},{"key":"10.1016\/j.displa.2026.103493_b19","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.giq.2013.04.003","article-title":"Open data policies, their implementation and impact: A framework for comparison","volume":"31","author":"Zuiderwijk","year":"2014","journal-title":"Gov. Inf. Q."},{"key":"10.1016\/j.displa.2026.103493_b20","article-title":"Learning to count objects in images","volume":"23","author":"Lempitsky","year":"2010","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.displa.2026.103493_b21","doi-asserted-by":"crossref","unstructured":"C. Zhang, H. Li, X. Wang, X. Yang, Cross-Scene Crowd Counting via Deep Convolutional Neural Networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2015, pp. 833\u2013841.","DOI":"10.1109\/CVPR.2015.7298684"},{"key":"10.1016\/j.displa.2026.103493_b22","doi-asserted-by":"crossref","unstructured":"X. Cao, Z. Wang, Y. Zhao, F. Su, Scale Aggregation Network for Accurate and Efficient Crowd Counting, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 734\u2013750.","DOI":"10.1007\/978-3-030-01228-1_45"},{"key":"10.1016\/j.displa.2026.103493_b23","doi-asserted-by":"crossref","unstructured":"Z. Ma, X. Wei, X. Hong, Y. Gong, Bayesian Loss for Crowd Count Estimation With Point Supervision, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2019, pp. 6142\u20136151.","DOI":"10.1109\/ICCV.2019.00624"},{"key":"10.1016\/j.displa.2026.103493_b24","doi-asserted-by":"crossref","unstructured":"J. Wan, W. Luo, B. Wu, A.B. Chan, W. Liu, Residual Regression With Semantic Prior for Crowd Counting, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2019, pp. 4036\u20134045.","DOI":"10.1109\/CVPR.2019.00416"},{"key":"10.1016\/j.displa.2026.103493_b25","first-page":"403","article-title":"Social Media as Beat: Tweets as a news source during the 2010 British and Dutch elections","volume":"6","author":"Broersma","year":"2012","journal-title":"Journal. Pr."},{"key":"10.1016\/j.displa.2026.103493_b26","doi-asserted-by":"crossref","unstructured":"H. Lin, Z. Ma, R. Ji, Y. Wang, X. Hong, Boosting Crowd Counting via Multifaceted Attention, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2022, pp. 19628\u201319637.","DOI":"10.1109\/CVPR52688.2022.01901"},{"key":"10.1016\/j.displa.2026.103493_b27","doi-asserted-by":"crossref","DOI":"10.1007\/s11432-021-3445-y","article-title":"TransCrowd: Weakly-supervised crowd counting with transformers","volume":"65","author":"Liang","year":"2022","journal-title":"Sci. China Inf. Sci."},{"key":"10.1016\/j.displa.2026.103493_b28","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"872","article-title":"Localization in the crowd with topological constraints","volume":"Vol. 35","author":"Abousamra","year":"2021"},{"key":"10.1016\/j.displa.2026.103493_b29","series-title":"Proceedings of the European Conference on Computer Vision","first-page":"213","article-title":"End-to-end object detection with transformers","volume":"Vol. 12346","author":"Carion","year":"2020"},{"key":"10.1016\/j.displa.2026.103493_b30","doi-asserted-by":"crossref","unstructured":"M.-R. Hsieh, Y.-L. Lin, W.H. Hsu, Drone-Based Object Counting by Spatially Regularized Regional Proposal Network, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2017, pp. 4145\u20134153.","DOI":"10.1109\/ICCV.2017.446"},{"key":"10.1016\/j.displa.2026.103493_b31","doi-asserted-by":"crossref","unstructured":"B. Sam, S. Surya, V. Babu, Switching Convolutional Neural Network for Crowd Counting, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 5744\u20135752.","DOI":"10.1109\/CVPR.2017.429"},{"key":"10.1016\/j.displa.2026.103493_b32","unstructured":"X. Zhu, W. Su, L. Lu, B. Li, X. Wang, J. Dai, Deformable DETR: Deformable Transformers for End-to-End Object Detection, in: Proceedings of the International Conference on Learning Representations, ICLR, 2021, pp. 1\u201316."},{"key":"10.1016\/j.displa.2026.103493_b33","doi-asserted-by":"crossref","unstructured":"W. Liu, M. Salzmann, P. Fua, Context-Aware Crowd Counting, in: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2019, pp. 5099\u20135108.","DOI":"10.1109\/CVPR.2019.00524"},{"key":"10.1016\/j.displa.2026.103493_b34","series-title":"C3 framework: An open-source PyTorch code for crowd counting","author":"Gao","year":"2019"},{"key":"10.1016\/j.displa.2026.103493_b35","series-title":"Automating the News: How Algorithms are Rewriting the Media","author":"Diakopoulos","year":"2019"},{"key":"10.1016\/j.displa.2026.103493_b36","series-title":"Deciding What\u2019s True : the Rise of Political Fact-Checking in American Journalism","author":"Graves","year":"2016"},{"key":"10.1016\/j.displa.2026.103493_b37","doi-asserted-by":"crossref","unstructured":"H. Idrees, I. Saleemi, C. Seibert, M. Shah, Multi-source Multi-scale Counting in Extremely Dense Crowd Images, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2013, pp. 2547\u20132554.","DOI":"10.1109\/CVPR.2013.329"},{"key":"10.1016\/j.displa.2026.103493_b38","series-title":"Proceedings of the IEEE International Conference on Digital Image Computing: Techniques and Applications","first-page":"81","article-title":"Crowd counting using multiple local features","author":"Ryan","year":"2009"},{"key":"10.1016\/j.displa.2026.103493_b39","doi-asserted-by":"crossref","unstructured":"H. Zhao, J. Shi, X. Qi, X. Wang, J. Jia, Pyramid Scene Parsing Network, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 2881\u20132890.","DOI":"10.1109\/CVPR.2017.660"},{"key":"10.1016\/j.displa.2026.103493_b40","doi-asserted-by":"crossref","unstructured":"X. Wang, R. Girshick, A. Gupta, K. He, Non-Local Neural Networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 7794\u20137803.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"10.1016\/j.displa.2026.103493_b41","unstructured":"A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, N. Houlsby, An image is worth 16x16 words: Transformers for image recognition at scale, in: International Conference on Learning Representations, ICLR, 2021, pp. 1\u201322."},{"key":"10.1016\/j.displa.2026.103493_b42","doi-asserted-by":"crossref","unstructured":"Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, B. Guo, Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows, in: Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, 2021, pp. 10012\u201310022.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"10.1016\/j.displa.2026.103493_b43","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1126\/science.1167742","article-title":"Computational social science","volume":"323","author":"Lazer","year":"2009","journal-title":"Science"},{"key":"10.1016\/j.displa.2026.103493_b44","series-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","first-page":"419","article-title":"Computational social science: exciting progress and future challenges","author":"Watts","year":"2016"},{"key":"10.1016\/j.displa.2026.103493_b45","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":"10.1016\/j.displa.2026.103493_b46","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1130","article-title":"Adaptive density map generation for crowd counting","author":"Wan","year":"2019"},{"key":"10.1016\/j.displa.2026.103493_b47","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.3390\/electronics10111293","article-title":"Crowd counting using end-to-end semantic image segmentation","volume":"10","author":"Khan","year":"2021","journal-title":"Electronics"},{"key":"10.1016\/j.displa.2026.103493_b48","series-title":"Advances in Neural Information Processing Systems","first-page":"2017","article-title":"Spatial transformer networks","author":"Jaderberg","year":"2015"},{"key":"10.1016\/j.displa.2026.103493_b49","doi-asserted-by":"crossref","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","article-title":"An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition","volume":"39","author":"Shi","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103493_b50","doi-asserted-by":"crossref","unstructured":"A. Kar, S. Tulsiani, J. Carreira, J. Malik, Category-Specific Object Reconstruction from a Single Image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2015, pp. 1966\u20131974.","DOI":"10.1109\/CVPR.2015.7298807"},{"key":"10.1016\/j.displa.2026.103493_b51","doi-asserted-by":"crossref","unstructured":"J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, Y. Wei, Deformable Convolutional Networks, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2017, pp. 764\u2013773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"10.1016\/j.displa.2026.103493_b52","doi-asserted-by":"crossref","unstructured":"T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, S. Belongie, Feature Pyramid Networks for Object Detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2017, pp. 2117\u20132125.","DOI":"10.1109\/CVPR.2017.106"},{"key":"10.1016\/j.displa.2026.103493_b53","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-18147-8","article-title":"Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning","volume":"11","author":"Song","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.displa.2026.103493_b54","doi-asserted-by":"crossref","first-page":"9935","DOI":"10.1038\/s41467-024-53993-w","article-title":"Automated estimation of cementitious sorptivity via computer vision","volume":"15","author":"Kabir","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.displa.2026.103493_b55","series-title":"Bayesian Learning for Neural Networks","author":"Neal","year":"2012"},{"key":"10.1016\/j.displa.2026.103493_b56","unstructured":"C. Guo, G. Pleiss, Y. Sun, K. Weinberger, On Calibration of Modern Neural Networks, in: Proceedings of the International Conference on Machine Learning, ICML, 2017, pp. 1321\u20131330."},{"key":"10.1016\/j.displa.2026.103493_b57","series-title":"Proceedings of the 2020 10th International Conference on Communication and Network Security (ICCNS))","first-page":"91","article-title":"A privacy-preserving framework for surveillance systems","author":"Wong","year":"2020"},{"key":"10.1016\/j.displa.2026.103493_b58","doi-asserted-by":"crossref","DOI":"10.2139\/ssrn.3909038","article-title":"Face off: Law enforcement use of face recognition technology","author":"Lynch","year":"2020","journal-title":"SSRN Electron. J."},{"key":"10.1016\/j.displa.2026.103493_b59","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MSP.2005.65","article-title":"Enabling video privacy through computer vision","volume":"3","author":"Senior","year":"2005","journal-title":"IEEE Secur. Priv. Mag."},{"key":"10.1016\/j.displa.2026.103493_b60","first-page":"809","article-title":"Algorithmic transparency in the news media","volume":"5","author":"Diakopoulos","year":"2016","journal-title":"Digit. Journal."},{"key":"10.1016\/j.displa.2026.103493_b61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/2053951716679679","article-title":"The ethics of algorithms: Mapping the debate","volume":"3","author":"Mittelstadt","year":"2016","journal-title":"Big Data Soc."},{"key":"10.1016\/j.displa.2026.103493_b62","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.displa.2026.103493_b63","doi-asserted-by":"crossref","unstructured":"H. Idrees, M. Tayyab, K. Athrey, D. Zhang, S. Al-Maadeed, N. Rajpoot, M. Shah, Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds, in: Proceedings of the European Conference on Computer Vision, ECCV, 2018, pp. 532\u2013546.","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"10.1016\/j.displa.2026.103493_b64","first-page":"2594","article-title":"JHU-CROWD++: Large-scale crowd counting dataset and a benchmark method","volume":"44","author":"Sindagi","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.displa.2026.103493_b65","doi-asserted-by":"crossref","unstructured":"X. Huang, S. Belongie, Arbitrary Style Transfer in Real-Time With Adaptive Instance Normalization, in: Proceedings of the IEEE International Conference on Computer Vision, ICCV, 2017, pp. 1501\u20131510.","DOI":"10.1109\/ICCV.2017.167"},{"key":"10.1016\/j.displa.2026.103493_b66","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.displa.2026.103493_b67","doi-asserted-by":"crossref","unstructured":"A. Kendall, Y. Gal, R. Cipolla, Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2018, pp. 7482\u20137491.","DOI":"10.1109\/CVPR.2018.00781"},{"key":"10.1016\/j.displa.2026.103493_b68","series-title":"International Conference on Machine Learning","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"Gal","year":"2016"}],"container-title":["Displays"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001563?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0141938226001563?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T01:44:19Z","timestamp":1783129459000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0141938226001563"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":68,"alternative-id":["S0141938226001563"],"URL":"https:\/\/doi.org\/10.1016\/j.displa.2026.103493","relation":{},"ISSN":["0141-9382"],"issn-type":[{"value":"0141-9382","type":"print"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A multi-task visual framework: Geometry-guided UAV crowd counting and localization for media practice","name":"articletitle","label":"Article Title"},{"value":"Displays","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.displa.2026.103493","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103493"}}