Hybrid Particle Filter and Mean Shift tracker
with adaptive transition model
This page presents the results of a tracking algorithm based on
a combination of Particle Filter and Mean Shift, and enhanced
with a new adaptive state transition model. Particle Filter is
robust to partial and total occlusions, can deal with
multi-modal pdf s and can recover lost tracks. However, its
complexity dramatically increases with the dimensionality of the
sampled pdf. Mean Shift has a low complexity, but is unable to
deal with multi-modal pdfs. To overcome these problems, the
proposed tracker first produces a smaller number of samples than
Particle Filter and then shifts the samples toward a close local
maximum using Mean Shift. The transition model predicts the
state based on adaptive variances. Experimental results show
that the combined tracker outperforms Particle Filter and Mean
Shift in terms of accuracy in estimating the target size and
position while generating 80% less samples than Particle Filter.