Next Article in Journal
Defuzzification Strategies for Fuzzy Classifications of Remote Sensing Data
Previous Article in Journal
Imaging Land Subsidence Induced by Groundwater Extraction in Beijing (China) Using Satellite Radar Interferometry
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(6), 470; doi:10.3390/rs8060470

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

German Aerospace Center (DLR), Remote Sensing Technology Institute, Wessling 82234, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Giles M. Foody, Guoqing Zhou and Prasad S. Thenkabail
Received: 24 February 2016 / Revised: 18 May 2016 / Accepted: 27 May 2016 / Published: 2 June 2016
View Full-Text   |   Download PDF [5032 KB, uploaded 2 June 2016]   |  

Abstract

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. View Full-Text
Keywords: texture; crowd detection; bag of words; fisher vectors; local binary patterns; gabor filter; aerial images; crowd density texture; crowd detection; bag of words; fisher vectors; local binary patterns; gabor filter; aerial images; crowd density
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Meynberg, O.; Cui, S.; Reinartz, P. Detection of High-Density Crowds in Aerial Images Using Texture Classification. Remote Sens. 2016, 8, 470.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top