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Journal of Applied Remote Sensing • Open Access

Feature analysis for detecting people from remotely sensed images
Author(s): Beril Sirmacek; Peter Reinartz

Paper Abstract

We propose a novel approach using airborne image sequences for detecting dense crowds and individuals. Although airborne images of this resolution range are not enough to see each person in detail, we can still notice a change of color and intensity components of the acquired image in the location where a person exists. Therefore, we propose a local feature detection-based probabilistic framework to detect people automatically. Extracted local features behave as observations of the probability density function (PDF) of the people locations to be estimated. Using an adaptive kernel density estimation method, we estimate the corresponding PDF. First, we use estimated PDF to detect boundaries of dense crowds. After that, using background information of dense crowds and previously extracted local features, we detect other people in noncrowd regions automatically for each image in the sequence. To test our crowd and people detection algorithm, we use airborne images taken over Munich during the Oktoberfest event, two different open-air concerts, and an outdoor festival. In addition, we apply tests on GeoEye-1 satellite images. Our experimental results indicate possible use of the algorithm in real-life mass events.

Paper Details

Date Published: 22 January 2013
PDF: 13 pages
J. Appl. Remote Sens. 7(1) 073594 doi: 10.1117/1.JRS.7.073594
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Beril Sirmacek, Deutsches Zentrum für Luft- und Raumfahrt e.V. (Germany)
Peter Reinartz, Deutsches Zentrum für Luft- und Raumfahrt e.V. (Germany)


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