Human tracking with mixtures of trees

S. Ioffe, D. Forsyth

Research output: Contribution to conferencePaperpeer-review


Tree-structured probabilistic models admit simple, fast inference. However, they are not well suited to phenomena such as occlusion, where multiple components of an object may disappear simultaneously. We address this problem with mixtures of trees, and demonstrate an efficient and compact representation of this mixture, which admits simple learning and inference algorithms. We use this method to build an automated tracker for Muybridge sequences of a variety of human activities. Tracking is difficult, because the temporal dependencies rule out simple inference methods. We show how to use our model for efficient inference, using a method that employs alternate spatial and temporal inference. The result is a tracker that (a) uses a very loose motion model, and so can track many different activities at a variable frame rate and (b) is entirely automatic.

Original languageEnglish (US)
Number of pages6
StatePublished - Jan 1 2001
Externally publishedYes
Event8th International Conference on Computer Vision - Vancouver, BC, United States
Duration: Jul 9 2001Jul 12 2001


Other8th International Conference on Computer Vision
CountryUnited States
CityVancouver, BC

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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