Visual contour tracking based on particle filters

—In the computer vision community, the Condensation algorithm has received considerable attention. Recently, it has been proven that the algorithm is one variant of particle filter (also known as sequential Monte Carlo filter, sequential importance sampling etc.). In sampling stage of Condensation, particles are drawn from the prior probability distribution of the state evolution transition, without making use of the most current observations, therefore, the algorithm demands a large number of particles and is computationally expensive. In this paper, a Kalman particle filter and an Unscented particle filter are presented to try to overcome the problem. These filters adopt sub-optimal proposal distributions, and use the Kalman filter or Unscented Kalman filter to incorporate the newest observation. This kind of sampling strategy can effectively steer the set of particles towards the region with high likelihood, and therefore, can considerably reduce the number of particles needed. Experiments with real image sequence are made to compare the performance of the three algorithms: Condensation, Kalman particle filter, and Unscented particle filter.