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Detection of Human Groups in Videos

  • Selçuk Sandıkcı
  • Svitlana Zinger
  • Peter H. N. de With
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6915)

Abstract

In this paper, we consider the problem of finding and localizing social human groups in videos, which can form a basis for further analysis and monitoring of groups in general. Our approach is motivated by the collective behavior of individuals which has a fundament in sociological studies. We design a detection-based multi-target tracking framework which is capable of handling short-term occlusions and producing stable trajectories. Human groups are discovered by clustering trajectories of individuals in an agglomerative fashion. A novel similarity function related to distances between group members, robustly measures the similarity of noisy trajectories. We have evaluated our approach on several test sequences and achieved acceptable miss rates (19.4%, 29.7% and 46.7%) at reasonable false positive detections per frame (0.129, 0.813 and 0.371). The relatively high miss rates are caused by a strict evaluation procedure, whereas the visual results are quite acceptable.

Keywords

Human Group Human Detection Cluster Threshold Group Detection Tracking Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Selçuk Sandıkcı
    • 1
  • Svitlana Zinger
    • 1
  • Peter H. N. de With
    • 1
  1. 1.Eindhoven University of TechnologyThe Netherlands

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