Learning Contextual Variations for Video Segmentation

  • Vincent Martin
  • Monique Thonnat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5008)


This paper deals with video segmentation in vision systems. We focus on the maintenance of background models in long-term videos of changing environment which is still a real challenge in video surveillance. We propose an original weakly supervised method for learning contextual variations in videos. Our approach uses a clustering algorithm to automatically identify different contexts based on image content analysis. Then, state-of-the-art video segmentation algorithms (e.g. codebook, MoG) are trained on each cluster. The goal is to achieve a dynamic selection of background models. We have experimented our approach on a long video sequence (24 hours). The presented results show the segmentation improvement of our approach compared to codebook and MoG.


video segmentation weakly supervised learning context awareness video surveillance cognitive vision 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Vincent Martin
    • 1
  • Monique Thonnat
    • 1
  1. 1.INRIA Sophia Antipolis, PULSAR project-teamSophia Antipolis

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