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

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

1112 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Title of host publication26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Publication statusPublished - 2008
Externally publishedYes

Conference

Title26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
PlaceUnited States
CityAnchorage, AK
Period23 - 28 June 2008

Abstract

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. ©2008 IEEE.

Citation Format(s)

Privacy preserving crowd monitoring: Counting people without people models or tracking. / Chan, Antoni B.; Liang, Zhang-Sheng John; Vasconcelos, Nuno.
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR. 2008. 4587569.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review