Decision Fusion of Shape and Motion Information Based on Bayesian Framework for Moving Object Classification in Image Sequences

  • Heungkyu Lee
  • JungHo Kim
  • June Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


This paper proposes decision fusion method of shape and motion information based on Bayesian framework for object classification in image sequences. This method is designed for intelligent information and surveillance guard robots to detect and track a suspicious person and vehicle within a security region. For reliable and stable classification of targets, multiple invariant feature vectors to more certainly discriminate between targets are required. To do this, shape and motion information are extracted using Fourier descriptor, gradients, and motion feature variation on spatial and temporal images, and then local decisions are performed respectively. Finally, global decision is done using decision fusion method based on Bayesian framework. The experimental results on the different test sequences showed that the proposed method obtained good classification result than any other ones using neural net and other fusion methods.


Fusion Method Local Decision Bayesian Framework Object Classification Motion Information 
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

  • Heungkyu Lee
    • 1
  • JungHo Kim
    • 2
  • June Kim
    • 3
  1. 1.Dept. of Computer ScienceSeokyeong UniversitySeoulKorea
  2. 2.Republic of Korea Navy 1st FleetKorea
  3. 3.Dept. of Information and Communication EngineeringSeokyeong UniversitySeoulKorea

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