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Scene Invariant Crowd Counting and Crowd Occupancy Analysis

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Video Analytics for Business Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 409))

Abstract

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.

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Author information

Authors and Affiliations

  1. Image and Video Laboratory, Queensland University of Technology, Brisbane, Australia

    David Ryan, Simon Denman, Sridha Sridharan & Clinton Fookes

Authors
  1. David Ryan
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  2. Simon Denman
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  3. Sridha Sridharan
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  4. Clinton Fookes
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Corresponding author

Correspondence to David Ryan .

Editor information

Editors and Affiliations

  1. Philips Research, High Tech Campus 36, Eindhoven, 5656 AE, Netherlands

    Caifeng Shan

  2. Mitsubishi Electric Research Laboratorie, Broadway 201, Cambridge, 02139, USA

    Fatih Porikli

  3. Dept. Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, United Kingdom

    Tao Xiang

  4. Dept. Computer Science, Queen Mary University of London, Mile End Rd., London, E1 4NS, United Kingdom

    Shaogang Gong

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Ryan, D., Denman, S., Sridharan, S., Fookes, C. (2012). Scene Invariant Crowd Counting and Crowd Occupancy Analysis. In: Shan, C., Porikli, F., Xiang, T., Gong, S. (eds) Video Analytics for Business Intelligence. Studies in Computational Intelligence, vol 409. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28598-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-28598-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28597-4

  • Online ISBN: 978-3-642-28598-1

  • eBook Packages: EngineeringEngineering (R0)

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