Cart (Loading....) | Create Account
Close category search window
 

Automatic crowd density and motion analysis in airborne image sequences based on a probabilistic framework

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sirmacek, B. ; German Aerosp. Center (DLR), Remote Sensing Technol. Inst., Wessling, Germany ; Reinartz, P.

Real-time monitoring of crowded regions has crucial importance to avoid overload of people in certain areas. Understanding behavioral dynamics of large people groups can also help to estimate future status of underground passages, public areas, or streets. In order to bring an automated solution to the problem, we propose a novel approach using airborne image sequences. Our approach depends on extraction of local features from invariant color components of the images. Using extracted local features as observations, we form probability density functions (pdf) for each image of input sequence which holds information about density of people. We introduce four measures to extract information about pdf characteristics. A change within the four measures over the image sequence gives important information about status of the crowds. Besides, we also use obtained pdfs to estimate main crowd motion directions. To test our algorithm, we use a stadium entrance image data set, and two festival area data sets taken from an airborne camera system. In order to be later able to reach real-time performance the algorithms use parameters which can be extracted directly from the image data. Experimental results indicate possible usage of the developed algorithms in real-life events.

Published in:

Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on

Date of Conference:

6-13 Nov. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.