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Segregation of Moving Objects Using Elastic Matching

  • Vishal Jain
  • Benjamin B. Kimia
  • Joseph L. Mundy
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3667)

Abstract

We present a method for figure-ground segregation of moving objects from monocular video sequences. The approach is based on tracking extracted contour fragments, in contrast to traditional approaches which rely on feature points, regions, and unorganized edge elements. Specifically, a notion of similarity between pairs of curve fragments appearing in two adjacent frames is developed and used to find the curve correspondence. This similarity metric is elastic in nature and in addition takes into account both a novel notion of transitions in curve fragments across video frames and an epipolar constraint. This yields a performance rate of 85% correct correspondence on a manually labeled set of frame pairs. The retrieved curve correspondence is then used to group curves in each frame into clusters based on the pairwise similarity of how they transform from one frame to the next. Results on video sequences of moving vehicles show that using curve fragments for tracking produces a richer segregation of figure from ground than current region or feature-based methods.

Keywords

Video Sequence Video Frame Adjacent Frame Epipolar Line Curve Match 
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 2006

Authors and Affiliations

  • Vishal Jain
    • 1
  • Benjamin B. Kimia
    • 1
  • Joseph L. Mundy
    • 1
  1. 1.Division of EngineeringBrown UniversityProvidenceUSA

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