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Understanding people motion in video sequences using Voronoi diagrams

Detecting and classifying groups

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Abstract

This work describes a model for understanding people motion in video sequences using Voronoi diagrams, focusing on group detection and classification. We use the position of each individual as a site for the Voronoi diagram at each frame, and determine the temporal evolution of some sociological and psychological parameters, such as distance to neighbors and personal spaces. These parameters are used to compute individual characteristics (such as perceived personal space and comfort levels), that are analyzed to detect the formation of groups and their classification as voluntary or involuntary. Experimental results based on videos obtained from real life as well as from a crowd simulator were analyzed and discussed.

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Notes

  1. A blob is a set of connected foreground pixels

  2. CROMOS Lab: http://www.inf.unisinos.br/~cromoslab

  3. In Fig. 7, frame 0 represents the first frame when a group was detected.

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Acknowledgments

This work was developed in collaboration with HP Brazil R&D. The authors would like to thank the anonymous reviewers for their fruitful contributions to improve this work.

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Authors and Affiliations

  1. Graduate School of Applied Computing, Universidade do Vale do Rio dos Sinos, Av. Unisinos 950, 93022-000, São Leopoldo, RS, Brazil

    Julio Cezar Silveira Jacques Jr., Adriana Braun, John Soldera & Cláudio Rosito Jung

  2. Graduate Program on Computer Science, Pontificia Universidade Catolica do Rio Grande do Sul, Av. Ipiranga, 6681 - Building 32 - Room 609, 90619-900, Porto Alegre, RS, Brazil

    Soraia Raupp Musse

Authors
  1. Julio Cezar Silveira Jacques Jr.
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  2. Adriana Braun
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  3. John Soldera
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  4. Soraia Raupp Musse
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  5. Cláudio Rosito Jung
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Corresponding author

Correspondence to Cláudio Rosito Jung.

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Jacques, J.C.S., Braun, A., Soldera, J. et al. Understanding people motion in video sequences using Voronoi diagrams. Pattern Anal Applic 10, 321–332 (2007). https://doi.org/10.1007/s10044-007-0070-1

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  • DOI: https://doi.org/10.1007/s10044-007-0070-1

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