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German Aerospace Center DLR, Remote Sensing Technology Institute, Wessling 82234, Germany





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Academic Editors: Giles M. Foody, Guoqing Zhou and Prasad S. Thenkabail

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





Autor: Oliver Meynberg * , Shiyong Cui and Peter Reinartz

Fuente: http://mdpi.com/



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