{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:14:11Z","timestamp":1753884851203,"version":"3.41.2"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Ltd","issue":"16","funder":[{"DOI":"10.13039\/100011038","name":"Office of the Director of National Intelligence","doi-asserted-by":"publisher","award":["HHM402-18-1-0007"],"award-info":[{"award-number":["HHM402-18-1-0007"]}],"id":[{"id":"10.13039\/100011038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1827505"],"award-info":[{"award-number":["1827505"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1737533"],"award-info":[{"award-number":["1737533"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100011038","name":"Office of the Director of National Intelligence","doi-asserted-by":"crossref","award":["HHM402-19-1-0003"],"award-info":[{"award-number":["HHM402-19-1-0003"]}],"id":[{"id":"10.13039\/100011038","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Bentley Systems, Incorporated","award":["Bentley-CUNY CRA 2017-2020"],"award-info":[{"award-number":["Bentley-CUNY CRA 2017-2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2021,12,30]]},"abstract":"<jats:p> Gatherings of thousands to millions of people frequently occur for an enormous variety of educational, social, sporting, and political events, and automated counting of these high-density crowds is useful for safety, management, and measuring significance of an event. In this work, we show that the regularly accepted labeling scheme of crowd density maps for training deep neural networks may not be the most effective one. We propose an alternative inverse k-nearest neighbor (i[Formula: see text]NN) map mechanism that, even when used directly in existing state-of-the-art network structures, shows superior performance. We also provide new network architecture mechanisms that we demonstrate in our own MUD-i[Formula: see text]NN network architecture, which uses multi-scale drop-in replacement upsampling via transposed convolutions to take full advantage of the provided i[Formula: see text]NN labeling. This upsampling combined with the i[Formula: see text]NN maps further improves crowd counting accuracy. We further analyze several variations of the i[Formula: see text]NN labeling mechanism, which apply transformations on the [Formula: see text]NN measure before generating the map, in order to consider the impact of camera perspective views, image resolutions, and the changing rates of the mapping functions. To alleviate the effects of crowd density changes in each image, we also introduce an attenuation mechanism in the i[Formula: see text]NN mapping. Experimentally, we show that inverse square root [Formula: see text]NN map variation (iR[Formula: see text]NN) provides the best performance. Discussions are provided on computational complexity, label resolutions, the gains in mapping and upsampling, and details of critical cases such as various crowd counts, uneven crowd densities, and crowd occlusions. <\/jats:p>","DOI":"10.1142\/s0218001421600120","type":"journal-article","created":{"date-parts":[[2021,9,17]],"date-time":"2021-09-17T05:20:18Z","timestamp":1631856018000},"source":"Crossref","is-referenced-by-count":3,"title":["Impact of Labeling Schemes on Dense Crowd Counting Using Convolutional Neural Networks with Multiscale Upsampling"],"prefix":"10.1142","volume":"35","author":[{"given":"Greg","family":"Olmschenk","sequence":"first","affiliation":[{"name":"Department of Computer Science, The Graduate Center, CUNY, 365 Fifth Avenue, New York, NY 10016, USA"},{"name":"NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The Graduate Center, CUNY, 365 Fifth Avenue, New York, NY 10016, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Computer Information Systems, Borough of Manhattan Community College, CUNY, 199 Chambers Street, New York, NY 10007, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9990-1137","authenticated-orcid":false,"given":"Zhigang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The Graduate Center, CUNY, 365 Fifth Avenue, New York, NY 10016, USA"},{"name":"Department of Computer Science, The City College of New York, CUNY, 160 Convent Avenue, New York, NY 10031, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2021,9,15]]},"reference":[{"key":"S0218001421600120BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_30"},{"key":"S0218001421600120BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"S0218001421600120BIB003","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_45"},{"key":"S0218001421600120BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2008.4587569"},{"key":"S0218001421600120BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.319"},{"key":"S0218001421600120BIB006","doi-asserted-by":"publisher","DOI":"10.5244\/C.26.21"},{"key":"S0218001421600120BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"S0218001421600120BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00141"},{"key":"S0218001421600120BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"S0218001421600120BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2013.329"},{"key":"S0218001421600120BIB011","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_33"},{"key":"S0218001421600120BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00476"},{"key":"S0218001421600120BIB013","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_34"},{"key":"S0218001421600120BIB014","first-page":"1324","volume-title":"Advances in Neural Information Processing Systems","author":"Lempitsky V.","year":"2010"},{"key":"S0218001421600120BIB015","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00120"},{"key":"S0218001421600120BIB016","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2009.204"},{"key":"S0218001421600120BIB017","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_17"},{"key":"S0218001421600120BIB018","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.429"},{"key":"S0218001421600120BIB019","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00550"},{"key":"S0218001421600120BIB020","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00564"},{"key":"S0218001421600120BIB021","doi-asserted-by":"publisher","DOI":"10.1109\/AVSS.2017.8078491"},{"key":"S0218001421600120BIB022","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995698"},{"key":"S0218001421600120BIB023","first-page":"90","volume-title":"Tenth IEEE Int. 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