On-line trajectory clustering for anomalous events detection

  • Authors:
  • C. Piciarelli;G. L. Foresti

  • Affiliations:
  • Department of Mathematics and Computer Science, University of Udine, Via delle Scienze 206, 33100 Udine, Italy;Department of Mathematics and Computer Science, University of Udine, Via delle Scienze 206, 33100 Udine, Italy

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2006

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Abstract

In this paper, we propose a trajectory clustering algorithm suited for video surveillance systems. Trajectories are clustered on-line, as the data are collected, and clusters are organized in a tree-like structure that, augmented with probability information, can be used to perform behaviour analysis, since it allows the identification of anomalous events.