Graph Mining for Object Tracking in Videos

  • Fabien Diot
  • Elisa Fromont
  • Baptiste Jeudy
  • Emmanuel Marilly
  • Olivier Martinot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7523)


This paper shows a concrete example of the use of graph mining for tracking objects in videos with moving cameras and without any contextual information on the objects to track. To make the mining algorithm efficient, we benefit from a video representation based on dynamic (evolving through time) planar graphs. We then define a number of constraints to efficiently find our so-called spatio-temporal graph patterns. Those patterns are linked through an occurrences graph to allow us to tackle occlusion or graph features instability problems in the video. Experiments on synthetic and real videos show that our method is effective and allows us to find relevant patterns for our tracking application.


Plane Graph Video Frame Object Tracking Pattern Mining Dynamic Graph 
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 2012

Authors and Affiliations

  • Fabien Diot
    • 1
    • 2
  • Elisa Fromont
    • 1
  • Baptiste Jeudy
    • 1
  • Emmanuel Marilly
    • 2
  • Olivier Martinot
    • 2
  1. 1.Laboratoire Hubert Curien, UMR CNRS 5516Université de Lyon, Université Jean Monnet de Saint-EtienneSaint-EtienneFrance
  2. 2.Alcatel-Lucent Bell LabsCentre de VillarceauxNozayFrance

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