  An image-based control strategy along with estimation of target motion is
developed to track dynamic targets without motion constraints. To the best of
our knowledge, this is the first work that utilizes a bounding box as image
features for tracking control and estimation of dynamic target without motion
constraint. The features generated from a You-Only-Look-Once (YOLO) deep neural
network can relax the assumption of continuous availability of the feature
points in most literature and minimize the gap for applications. The challenges
are that the motion pattern of the target is unknown and modeling its dynamics
is infeasible. To resolve these issues, the dynamics of the target is modeled
by a constant-velocity model and is employed as a process model in the
unscented Kalman filter (UKF), but process noise is uncertain and sensitive to
system instability. To ensure convergence of the estimate error, the noise
covariance matrix is estimated according to history data within a moving
window. The estimated motion from the UKF is implemented as a feedforward term
in the developed controller, so that tracking performance is enhanced.
Simulations are demonstrated to verify the efficacy of the developed estimator
and controller.
