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Online Moving Camera Background Subtraction

  • Ali Elqursh
  • Ahmed Elgammal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

Recently several methods for background subtraction from moving camera were proposed. They use bottom up cues to segment video frames into foreground and background regions. Due to this lack of explicit models, they can easily fail to detect a foreground object when such cues are ambiguous in certain parts of the video. This becomes even more challenging when videos need to be processed online. We present a method which enables learning of pixel based models for foreground and background regions and, in addition, segments each frame in an online framework. The method uses long term trajectories along with a Bayesian filtering framework to estimate motion and appearance models. We compare our method to previous approaches and show results on challenging video sequences.

Keywords

Motion Vector Gaussian Mixture Model Background Subtraction Motion Model Appearance Model 
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

  • Ali Elqursh
    • 1
  • Ahmed Elgammal
    • 1
  1. 1.Rutgers UniversityUSA

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