Multi-sensor Fusion Using Dempster’s Theory of Evidence for Video Segmentation

  • Björn Scheuermann
  • Sotirios Gkoutelitsas
  • Bodo Rosenhahn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)


Segmentation of image sequences is a challenging task in computer vision. Time-of-Flight cameras provide additional information, namely depth, that can be integrated as an additional feature in a segmentation approach. Typically, the depth information is less sensitive to environment changes. Combined with appearance, this yields a more robust segmentation method. Motivated by the fact that a simple combination of two information sources might not be the best solution, we propose a novel scheme based on Dempster’s theory of evidence. In contrast to existing methods, the use of Dempster’s theory of evidence allows to model inaccuracy and uncertainty. The inaccuracy of the information is influenced by an adaptive weight, that provides a measurement of how reliable a certain information might be. We compare our method with others on a publicly available set of image sequences. We show that the use of our proposed fusion scheme improves the segmentation.


Gaussian Mixture Model Segmentation Result Depth Information Basic Probability Assignment Video Segmentation 
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 2013

Authors and Affiliations

  • Björn Scheuermann
    • 1
  • Sotirios Gkoutelitsas
    • 1
  • Bodo Rosenhahn
    • 1
  1. 1.Institut für Informationsverarbeitung (TNT)Leibniz Universität HannoverGermany

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