Detection of High-Density Crowds in Aerial Images Using Texture Classification
AbstractAutomatic 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
Scifeed alert for new publicationsNever 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
Meynberg, O.; Cui, S.; Reinartz, P. Detection of High-Density Crowds in Aerial Images Using Texture Classification. Remote Sens. 2016, 8, 470.
Meynberg O, Cui S, Reinartz P. Detection of High-Density Crowds in Aerial Images Using Texture Classification. Remote Sensing. 2016; 8(6):470.Chicago/Turabian Style
Meynberg, Oliver; Cui, Shiyong; Reinartz, Peter. 2016. "Detection of High-Density Crowds in Aerial Images Using Texture Classification." Remote Sens. 8, no. 6: 470.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.