  Autonomous robots enjoy a wide popularity nowadays and have been applied in
many applications, such as home security, entertainment, delivery, navigation
and guidance. It is vital to robots to track objects accurately in these
applications, so it is necessary to focus on tracking algorithms to improve the
robustness and accuracy. In this paper, we propose a robust object tracking
algorithm based on a hierarchical ensemble framework which can incorporate
information including individual pixel features, local patches and holistic
target models. The framework combines multiple ensemble models simultaneously
instead of using a single ensemble model individually. A discriminative model
which accounts for the matching degree of local patches is adopted via a bottom
ensemble layer, and a generative model which exploits holistic templates is
used to search for the object through the middle ensemble layer as well as an
adaptive Kalman filter. We test the proposed tracker on challenging benchmark
image sequences. Both qualitative and quantitative evaluations demonstrate that
the proposed tracker performs superiorly against several state-of-the-art
algorithms, especially when the appearance changes dramatically and the
occlusions occur.
