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Counting people in the crowd using social media images for crowd management in city events

Author

Listed:
  • V. X. Gong

    (Delft University of Technology
    Delft University of Technology)

  • W. Daamen

    (Delft University of Technology)

  • A. Bozzon

    (Delft University of Technology)

  • S. P. Hoogendoorn

    (Delft University of Technology)

Abstract

City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.

Suggested Citation

  • V. X. Gong & W. Daamen & A. Bozzon & S. P. Hoogendoorn, 2021. "Counting people in the crowd using social media images for crowd management in city events," Transportation, Springer, vol. 48(6), pages 3085-3119, December.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:6:d:10.1007_s11116-020-10159-z
    DOI: 10.1007/s11116-020-10159-z
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    References listed on IDEAS

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    1. Duives, Dorine C. & Daamen, Winnie & Hoogendoorn, Serge P., 2015. "Quantification of the level of crowdedness for pedestrian movements," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 427(C), pages 162-180.
    2. Dirk Helbing & Lubos Buzna & Anders Johansson & Torsten Werner, 2005. "Self-Organized Pedestrian Crowd Dynamics: Experiments, Simulations, and Design Solutions," Transportation Science, INFORMS, vol. 39(1), pages 1-24, February.
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