Moving Objects Detection from Video Sequences Using Fuzzy Edge Incorporated Markov Random Field Modeling and Local Histogram Matching

  • Badri Narayan Subudhi
  • Ashish Ghosh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)


In this article, we put forward a novel region matching based motion estimation scheme to detect objects with accurate boundaries from video sequences. We have proposed a fuzzy edge incorporated Markov Random Field (MRF) model based spatial segmentation scheme that is able even to identify the blurred boundaries of objects in a scene. Expectation Maximization (EM) algorithm is used to estimate the MRF model parameters. To reduce the complexity of searching, a new scheme is proposed to get a rough knowledge of maximum possible shift of objects from one frame to another by finding the amount of shift in positions of the centroid. Moving objects in the scene are detected by the proposed χ 2-test based local histogram matching. It is noticed that the proposed scheme provides better results with less object background misclassification as compared to optical flow and label fusion based techniques.


Video Sequence Local Binary Pattern Image Frame Markov Random Field Target Frame 
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

  • Badri Narayan Subudhi
    • 1
  • Ashish Ghosh
    • 1
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia

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