Scene invariant crowd counting and crowd occupancy analysis

Ryan, David, Denman, Simon, Sridharan, Sridha, & Fookes, Clinton B. (2012) Scene invariant crowd counting and crowd occupancy analysis. In Video Analytics for Business Intelligence [Studies in Computational Intelligence, Volume 409]. Springer-Verlag, Germany, pp. 161-198.

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In public places, crowd size may be an indicator of congestion, delay, instability, or of abnormal events, such as a fight, riot or emergency. Crowd related information can also provide important business intelligence such as the distribution of people throughout spaces, throughput rates, and local densities.

A major drawback of many crowd counting approaches is their reliance on large numbers of holistic features, training data requirements of hundreds or thousands of frames per camera, and that each camera must be trained separately. This makes deployment in large multi-camera environments such as shopping centres very costly and difficult.

In this chapter, we present a novel scene-invariant crowd counting algorithm that uses local features to monitor crowd size. The use of local features allows the proposed algorithm to calculate local occupancy statistics, scale to conditions which are unseen in the training data, and be trained on significantly less data.

Scene invariance is achieved through the use of camera calibration, allowing the system to be trained on one or more viewpoints and then deployed on any number of new cameras for testing without further training. A pre-trained system could then be used as a ‘turn-key’ solution for crowd counting across a wide range of environments, eliminating many of the costly barriers to deployment which currently exist.

Impact and interest:

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ID Code: 47054
Item Type: Book Chapter
Additional URLs:
Keywords: crowd counting, scene invariant, local features, density estimation, crowd monitoring
DOI: 10.1007/978-3-642-28598-1_6
ISBN: 978-3-642-28597-4
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Image Processing (080106)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Past > Institutes > Information Security Institute
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2012 Springer-Verlag
Copyright Statement: The original publication is available at SpringerLink
Deposited On: 17 Nov 2011 23:03
Last Modified: 10 Oct 2015 07:46

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