International Journal of Computer Vision

, Volume 53, Issue 3, pp 199–223

Human Body Model Acquisition and Tracking Using Voxel Data


  • Ivana Mikić
    • Q3DM, Inc.
  • Mohan Trivedi
    • Department of Electrical and Computer EngineeringUniversity of California, San Diego
  • Edward Hunter
    • Q3DM, Inc.
  • Pamela Cosman
    • Department of Electrical and Computer EngineeringUniversity of California, San Diego

DOI: 10.1023/A:1023012723347

Cite this article as:
Mikić, I., Trivedi, M., Hunter, E. et al. International Journal of Computer Vision (2003) 53: 199. doi:10.1023/A:1023012723347


We present an integrated system for automatic acquisition of the human body model and motion tracking using input from multiple synchronized video streams. The video frames are segmented and the 3D voxel reconstructions of the human body shape in each frame are computed from the foreground silhouettes. These reconstructions are then used as input to the model acquisition and tracking algorithms.

The human body model consists of ellipsoids and cylinders and is described using the twists framework resulting in a non-redundant set of model parameters. Model acquisition starts with a simple body part localization procedure based on template fitting and growing, which uses prior knowledge of average body part shapes and dimensions. The initial model is then refined using a Bayesian network that imposes human body proportions onto the body part size estimates. The tracker is an extended Kalman filter that estimates model parameters based on the measurements made on the labeled voxel data. A voxel labeling procedure that handles large frame-to-frame displacements was designed resulting in very robust tracking performance.

Extensive evaluation shows that the system performs very reliably on sequences that include different types of motion such as walking, sitting, dancing, running and jumping and people of very different body sizes, from a nine year old girl to a tall adult male.

human body model acquisitionmotion capturepose estimation

Copyright information

© Kluwer Academic Publishers 2003