Fast upper body joint tracking using Kinect pose priors

Michael Burke, Joan Lasenby

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)


Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and instead attempt to incorporate these constraints through priors obtained directly from training data, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this information with a random walk transition model to obtain an upper body model that can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body.

Original languageEnglish
Title of host publicationArticulated Motion and Deformable Objects
Subtitle of host publication8th International Conference, AMDO 2014 Palma de Mallorca, Spain, July 16-18, 2014 Proceedings
EditorsFrancisco José Perales, José Santos-Victor
Place of PublicationCham Switzerland
Number of pages12
ISBN (Electronic)9783319088495
ISBN (Print)9783319088488
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Articulated Motion and Deformable Objects 2014 - Palma de Mallorca, Spain
Duration: 16 Jul 201418 Jul 2014
Conference number: 8th (Proceedings)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Articulated Motion and Deformable Objects 2014
Abbreviated titleAMDO 2014
CityPalma de Mallorca
Internet address


  • Human pose estimation
  • Kinect
  • Mixture Kalman filter
  • Monocular vision

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