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Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 pp 329–345Cite as

Unsupervised Crowd Counting

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10115))

Abstract

Most crowd counting methods rely on training with labeled data to learn a mapping between image features and the number of people in the scene. However, the nature of this mapping may change as a function of the scene, camera parameters, illumination etc., limiting the ability of such supervised systems to generalize to novel conditions. Here we propose an alternative, unsupervised strategy. The approach is anchored on a 3D simulation that automatically learns how groups of people appear in the image. Central to the simulation is an auto-scaling step that uses the video data to infer the distribution of projected sizes of individual people detected in the scene, allowing the simulation to adapt to the signal processing parameters of the current viewing scenario. Since the simulation need only run periodically, the method is efficient and scalable to large crowds. We evaluate the method on two datasets and show that it performs well relative to supervised methods.

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Acknowledgement

This research was supported by an NSERC Discovery research grant and by the NSERC CREATE training program in Vision Science and Applications.

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Authors and Affiliations

  1. Centre for Vision Research, York University, Toronto, Canada

    Nada Elassal & James H. Elder

Authors
  1. Nada Elassal
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  2. James H. Elder
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Correspondence to Nada Elassal .

Editor information

Editors and Affiliations

  1. National Tsing Hua University, Hsinchu, Taiwan

    Shang-Hong Lai

  2. Graz University of Technology, Graz, Austria

    Vincent Lepetit

  3. Drexel University, Philadelphia, Pennsylvania, USA

    Ko Nishino

  4. The University of Tokyo, Tokyo, Japan

    Yoichi Sato

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Elassal, N., Elder, J.H. (2017). Unsupervised Crowd Counting. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10115. Springer, Cham. https://doi.org/10.1007/978-3-319-54193-8_21

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  • DOI: https://doi.org/10.1007/978-3-319-54193-8_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54192-1

  • Online ISBN: 978-3-319-54193-8

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