  Robust visual tracking for long video sequences is a research area that has
many important applications. The main challenges include how the target image
can be modeled and how this model can be updated. In this paper, we model the
target using a covariance descriptor, as this descriptor is robust to problems
such as pixel-pixel misalignment, pose and illumination changes, that commonly
occur in visual tracking. We model the changes in the template using a
generative process. We introduce a new dynamical model for the template update
using a random walk on the Riemannian manifold where the covariance descriptors
lie in. This is done using log-transformed space of the manifold to free the
constraints imposed inherently by positive semidefinite matrices. Modeling
template variations and poses kinetics together in the state space enables us
to jointly quantify the uncertainties relating to the kinematic states and the
template in a principled way. Finally, the sequential inference of the
posterior distribution of the kinematic states and the template is done using a
particle filter. Our results shows that this principled approach can be robust
to changes in illumination, poses and spatial affine transformation. In the
experiments, our method outperformed the current state-of-the-art algorithm -
the incremental Principal Component Analysis method, particularly when a target
underwent fast poses changes and also maintained a comparable performance in
stable target tracking cases.
