Privacy preserving crowd monitoring: Counting people without people models or tracking

Antoni B. Chan; Zhang-Sheng John Liang; Nuno Vasconcelos

doi:10.1109/CVPR.2008.4587569

C
CVPR
2008
2008 IEEE Conference on Computer Vision and Pattern Recognition
Abstract - Privacy preserving crowd monitoring: Counting people without people models or tracking

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2008 IEEE Conference on Computer Vision and Pattern Recognition

Privacy preserving crowd monitoring: Counting people without people models or tracking

Anchorage, AK, USA

June 23-June 28

ISBN: 978-1-4244-2242-5

	ASCII Text	x
	Antoni B. Chan, Zhang-Sheng John Liang, Nuno Vasconcelos, "Privacy preserving crowd monitoring: Counting people without people models or tracking," 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-7, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.

	BibTex	x
	@article{ 10.1109/CVPR.2008.4587569, author = {Antoni B. Chan and Zhang-Sheng John Liang and Nuno Vasconcelos}, title = {Privacy preserving crowd monitoring: Counting people without people models or tracking}, journal ={2012 IEEE Conference on Computer Vision and Pattern Recognition}, volume = {0}, year = {2008}, isbn = {978-1-4244-2242-5}, pages = {1-7}, doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587569}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - 2012 IEEE Conference on Computer Vision and Pattern Recognition TI - Privacy preserving crowd monitoring: Counting people without people models or tracking SN - 978-1-4244-2242-5 SP1 EP7 A1 - Antoni B. Chan, A1 - Zhang-Sheng John Liang, A1 - Nuno Vasconcelos, PY - 2008 VL - 0 JA - 2012 IEEE Conference on Computer Vision and Pattern Recognition ER -

Antoni B. Chan, Electrical and Computer Engineering Department, University of California, San Diego, USA

Zhang-Sheng John Liang, Electrical and Computer Engineering Department, University of California, San Diego, USA

Nuno Vasconcelos, Electrical and Computer Engineering Department, University of California, San Diego, USA

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CVPR.2008.4587569

We present a privacy-preserving system for estimating the size of inhomogeneous crowds, composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking. First, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic textures motion model. Second, a set of simple holistic features is extracted from each segmented region, and the correspondence between features and the number of people per segment is learned with Gaussian Process regression. We validate both the crowd segmentation algorithm, and the crowd counting system, on a large pedestrian dataset (2000 frames of video, containing 49,885 total pedestrian instances). Finally, we present results of the system running on a full hour of video.

Citation:

Antoni B. Chan, Zhang-Sheng John Liang, Nuno Vasconcelos, "Privacy preserving crowd monitoring: Counting people without people models or tracking," cvpr, pp.1-7, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008

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