{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:43:50Z","timestamp":1768315430118,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2016,6,2]],"date-time":"2016-06-02T00:00:00Z","timestamp":1464825600000},"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>Automatic crowd detection in aerial images is certainly a useful source of information to prevent crowd disasters in large complex scenarios of mass events. A number of publications employ regression-based methods for crowd counting and crowd density estimation. However, these methods work only when a correct manual count is available to serve as a reference. Therefore, it is the objective of this paper to detect high-density crowds in aerial images, where counting\u2013 or regression\u2013based approaches would fail. We compare two texture\u2013classification methodologies on a dataset of aerial image patches which are grouped into ranges of different crowd density. These methodologies are: (1) a Bag\u2013of\u2013words (BoW) model with two alternative local features encoded as Improved Fisher Vectors and (2) features based on a Gabor filter bank. Our results show that a classifier using either BoW or Gabor features can detect crowded image regions with 97% classification accuracy. In our tests of four classes of different crowd-density ranges, BoW\u2013based features have a 5%\u201312% better accuracy than Gabor.<\/jats:p>","DOI":"10.3390\/rs8060470","type":"journal-article","created":{"date-parts":[[2016,6,2]],"date-time":"2016-06-02T10:19:08Z","timestamp":1464862748000},"page":"470","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Detection of High-Density Crowds in Aerial Images Using Texture Classification"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7218-9840","authenticated-orcid":false,"given":"Oliver","family":"Meynberg","sequence":"first","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, Wessling 82234, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shiyong","family":"Cui","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, Wessling 82234, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8122-1475","authenticated-orcid":false,"given":"Peter","family":"Reinartz","sequence":"additional","affiliation":[{"name":"German Aerospace Center (DLR), Remote Sensing Technology Institute, Wessling 82234, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,6,2]]},"reference":[{"key":"ref_1","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. 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