Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Semi-automatic people counting in aerial images of large crowds

: Herrmann, Christian; Metzler, Jürgen; Willersinn, Dieter

Volltext urn:nbn:de:0011-n-2721238 (1.4 MByte PDF)
MD5 Fingerprint: b4a4bb718df0cd3b0f39efa3ddd33d8d
Copyright Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Erstellt am: 18.12.2013

Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
Electro-Optical Remote Sensing, Photonic Technologies, and Applications VI : 24. Sept. 2012, Edinburgh, United Kingom
Bellingham, WA: SPIE, 2012 (Proceedings of SPIE 8542)
ISBN: 978-0-8194-9283-8
Paper 85420Q
Conference "Electro-Optical Remote Sensing, Photonic Technologies, and Applications" <6, 2012, Edinburgh>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()

Counting people in crowds is a common problem in visual surveillance. Many solutions are just designed to count less than one hundred people. Only few systems have been tested on large crowds of several hundred people and no known counting system has been tested on crowds of several thousand people. Furthermore, none of these large scale systems delivers people's positions, they just estimate the number. But having the position of people would be a large benefit, since this would enable a human observer to carry out a plausibility check. In addition, most approaches require video data as input or a scene model. In order to generally solve the problem, these assumptions must not be made. We propose a system that can count people on single aerial images including mosaic images generated from video data. No assumptions about crowd density will be made, i. e. the system has to work from low to very high density. The main challenge is the large variety of possible input data. Typical scenarios would be public events such as demonstrations or open air concerts. Our system uses a model-based detection of individual humans. This includes the determination of their positions and the total number. In order to cope with the given challenges we divide our system into three steps: foreground segmentation, person size determination and person detection. We evaluate our proposed system on a variety of aerial images showing large crowds with up to several thousand people.