Tracking through Singularities and Discontinuities by Random Sampling

Some issues in markerless tracking of human body motion are addressed. Extended Kalman filters have commonly been applied to kinematic variables, to combine predictions consistent with plausible motion, with the incoming stream of visual measurements. Kalman filtering is applicable only when the underlying distribution is approximately Gaussian. Often, this assumption proves remarkably robust. There are two pervasive circumstances under which the Gaussianity assumption can break down. The first is kinematic singularity, and the second is at joint endstops. Failure of Kalman filtering under these circumstance is illustrated. The non-Gaussian nature of the distributions is demonstrated experimentally by means of Monte-Carlo simulation. Random simulation — particle filtering or Condensation — proves to provide a robust alternative algorithm for tracking that can also deal with these difficult conditions.