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Detection of High-Density Crowds in Aerial Images Using Texture Classification

Meynberg, Oliver and Cui, Shiyong and Reinartz, Peter (2016) Detection of High-Density Crowds in Aerial Images Using Texture Classification. Remote Sensing, 8 (6), pp. 1-17. Multidisciplinary Digital Publishing Institute (MDPI). DOI: 10.3390/rs8060470 ISSN 2072-4292

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Official URL: http://www.mdpi.com/2072-4292/8/6/470


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– or regression–based approaches would fail. We compare two texture–classification methodologies on a dataset of aerial image patches which are grouped into ranges of different crowd density. These methodologies are: (1) a Bag–of–words (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–based features have a 5%–12% better accuracy than Gabor.

Item URL in elib:https://elib.dlr.de/104803/
Document Type:Article
Title:Detection of High-Density Crowds in Aerial Images Using Texture Classification
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Meynberg, Oliveroliver.meynberg (at) dlr.deUNSPECIFIED
Cui, Shiyongshiyong.cui (at) dlr.deUNSPECIFIED
Reinartz, Peterpeter.reinartz (at) dlr.deUNSPECIFIED
Date:2 June 2016
Journal or Publication Title:Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In ISI Web of Science:Yes
DOI :10.3390/rs8060470
Page Range:pp. 1-17
Foody, Giles M.University of Nottingham, UK
Zhou, Guoqinggzhou@glut.edu.cn
Thenkabail, Prasad S.thenkabail@gmail.com
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Keywords:texture; crowd detection; bag of words; fisher vectors; local binary patterns; gabor filter; aerial images; crowd density
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited On:04 Jul 2016 12:13
Last Modified:14 Dec 2019 04:26

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