| This paper describes an unified probabilistic framework for appearance
based tracking of rigid and non-rigid objects.A spatio-temporal dependent
shape/texture Eigenspace and mixture of diagonal gaussians are learned
in a Hidden Markov Model(HMM) like structure to better constrain the model
and for recognition purposes. Particle filtering is used to track the object
while switching between different shape/texture models. This framework
allows recognition and temporal segmentation of activities. Additionally
an automatic stochastic initialization is proposed, the number of states
in the HMM are selected based on the Akaike Information Criterion and comparison
with deterministic tracking for 2D models is discussed. Preliminary results
of eye-tracking, lip-tracking and temporal segmentation of mouth events
are presented. |
