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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
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
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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