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Crowd detection from still images

Arandjelovic, Ognjen 2008, Crowd detection from still images, in BMVC 2008 : Proceedings of the British machine vision association conference 2008, BMVA Press, Manchester, England, pp. 1-10, doi: 10.5244/C.22.53.

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Title Crowd detection from still images
Author(s) Arandjelovic, Ognjen
Conference name British Machine Vision. Conference (2008 : Leeds, England)
Conference location Leeds, England
Conference dates 1-4 Sept. 2008
Title of proceedings BMVC 2008 : Proceedings of the British machine vision association conference 2008
Editor(s) [Unknown]
Publication date 2008
Conference series British Machine Vision Conference
Start page 1
End page 10
Total pages 10
Publisher BMVA Press
Place of publication Manchester, England
Summary The analysis of human crowds has widespread uses from law enforcement to urban engineering and traffic management. All of these require a crowd to first be detected, which is the problem addressed in this paper. Given an image, the algorithm we propose segments it into crowd and non-crowd regions. The main idea is to capture two key properties of crowds: (i) on a narrow scale, its basic element should look like a human (only weakly so, due to low resolution, occlusion, clothing variation etc.), while (ii) on a larger scale, a crowd inherently contains repetitive appearance elements. Our method exploits this by building a pyramid of sliding windows and quantifying how “crowd-like” each level of the pyramid is using an underlying statistical model based on quantized SIFT features. The two aforementioned crowd properties are captured by the resulting feature vector of window responses, describing the degree of crowd-like appearance around an image location as the surrounding spatial extent is increased.
ISBN 01901725367
Language eng
DOI 10.5244/C.22.53
Field of Research 080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2008, BMVA Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058427

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.