  In this paper, a novel image moments based model for shape estimation and
tracking of an object moving with a complex trajectory is presented. The camera
is assumed to be stationary looking at a moving object. Point features inside
the object are sampled as measurements. An ellipsoidal approximation of the
shape is assumed as a primitive shape. The shape of an ellipse is estimated
using a combination of image moments. Dynamic model of image moments when the
object moves under the constant velocity or coordinated turn motion model is
derived as a function for the shape estimation of the object. An Unscented
Kalman Filter-Interacting Multiple Model (UKF-IMM) filter algorithm is applied
to estimate the shape of the object (approximated as an ellipse) and track its
position and velocity. A likelihood function based on average log-likelihood is
derived for the IMM filter. Simulation results of the proposed UKF-IMM
algorithm with the image moments based models are presented that show the
estimations of the shape of the object moving in complex trajectories.
Comparison results, using intersection over union (IOU), and position and
velocity root mean square errors (RMSE) as metrics, with a benchmark algorithm
from literature are presented. Results on real image data captured from the
quadcopter are also presented.
