Social Groups Detection in Crowd through Shape-Augmented Structured Learning

  • Francesco Solera
  • Simone Calderara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

Most of the behaviors people exhibit while being part of a crowd are social processes that tend to emerge among groups and as a consequence, detecting groups in crowds is becoming an important issue in modern behavior analysis. We propose a supervised correlation clustering technique that employs Structural SVM and a proxemic based feature to learn how to partition people trajectories in groups, by injecting in the model socially plausible shape configurations. By taking into account social groups patterns, the system is able to outperform state of the art methods on two publicly available benchmark sets of videos.

Keywords

group detection proxemic theory Structural SVM 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesco Solera
    • 1
  • Simone Calderara
    • 1
  1. 1.DIEF University of Modena and Reggio EmiliaItaly

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