Crowd detection from still images

Oggie Arandelovic*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

48 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationBMVC 2008 - Proceedings of the British Machine Vision Conference 2008
PublisherBritish Machine Vision Association, BMVA
DOIs
Publication statusPublished - 2008
Event2008 19th British Machine Vision Conference, BMVC 2008 - Leeds, United Kingdom
Duration: 1 Sept 20084 Sept 2008

Conference

Conference2008 19th British Machine Vision Conference, BMVC 2008
Country/TerritoryUnited Kingdom
CityLeeds
Period1/09/084/09/08

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