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Image processing techniques for crowd density estimation using a reference image

  • Outdoor Computer Vision
  • Conference paper
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Book cover Recent Developments in Computer Vision (ACCV 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1035))

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Abstract

There are still significant problems in the planning, design and management of public facilities subject to dense pedestrian traffic. The automation of data collection and analysis of crowd behaviour is increasingly desirable in design of facilities and long-term site management using image processing techniques with existing closed-circuit television systems. We have investigated a number of techniques for crowd density estimation, movement estimation, incident detection and their relative merits using image processing. This paper presents techniques for background generation and calibration to enhance the previously-developed method of crowd density estimation using a reference image. An intensity region related to the average pixel intensity of each image in a sequence of crowd images is used to segment background pixels for generating a background image without pedestrians. The calibration approach, with which a previously-established relationship between image parameters and crowd density at one site can be used to estimate crowd density at various sites, involves calibration of the crowd image as opposed to calibration of the camera. Both techniques may be used in other surveillance systems such as vehicle monitoring.

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References

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Authors and Affiliations

  1. Vision and Robotics Laboratory Department of Electronic and Electrical Engineering, King's College London, WC2R 2LS, Strand, London, UK

    Jia Hong Yin, Sergio A. Velastin & Anthony C. Davies

Authors
  1. Jia Hong Yin
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  2. Sergio A. Velastin
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  3. Anthony C. Davies
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Editor information

Stan Z. Li Dinesh P. Mital Eam Khwang Teoh Han Wang

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© 1996 Springer-Verlag Berlin Heidelberg

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Yin, J.H., Velastin, S.A., Davies, A.C. (1996). Image processing techniques for crowd density estimation using a reference image. In: Li, S.Z., Mital, D.P., Teoh, E.K., Wang, H. (eds) Recent Developments in Computer Vision. ACCV 1995. Lecture Notes in Computer Science, vol 1035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60793-5_102

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  • DOI: https://doi.org/10.1007/3-540-60793-5_102

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60793-9

  • Online ISBN: 978-3-540-49448-5

  • eBook Packages: Springer Book Archive

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