Segmentations of Spatio-Temporal Images by Spatio-Temporal Markov Random Field Model

  • Shunsuke Kamijo
  • Katsushi Ikeuchi
  • Masao Sakauchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2134)


There have been many successful researches on image segmentations that employ Markov Random Field model. However, most of them were interested in two-dimensional MRF, or spatial MRF, and very few researches are interested in three-dimensional MRF model. Generally, ’three-dimensional’ have two meaning, that are spatially three-dimensional and spatio-temporal. In this paper, we especially are interested in segmentations of spatio-temporal images which appears to be equivalent to tracking problem of moving objects such as vehicles etc. For that purpose, by extending usual two-dimensional MRF, we defined a dedicated three-dimensional MRF which we defined as Spatio-Temporal MRF model(S-T MRF). This S-T MRF models a tracking problem by determining labels of groups of pixels by referring to their texture and labeling correlations along the temporal axis as well as the x-y image axes.Although vehicles severely occlude each other in general traffic images,segmentation boundaries of vehicle regions will be determined precisely by this S-T MRF optimizing such boundaries through spatio-temporal images. Consequently, it was proved that the algorithm has performed 95% success of tracking in middle-angle image at an intersection and 91% success in low-angle and front-view images at a highway junction.


Motion Vector Markov Random Field Temporal Axis Tracking Problem Consecutive Image 
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 2001

Authors and Affiliations

  • Shunsuke Kamijo
    • 1
  • Katsushi Ikeuchi
    • 1
  • Masao Sakauchi
    • 1
  1. 1.Institute of Industrial ScienceUniversity of TokyoTokyoJapan

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