Damien Jade Duff, Jeremy Wyatt and Rustam Stolkin.
Motion Estimation Using Physical Simulation.
In IEEE International Conference on Robotics and Automation (ICRA), pages 1511--1517, IEEE.
We consider the task of monocular visual motion
estimation from video image sequences. We hypothesise that
performance on the task can be improved by incorporating an
understanding of physically likely and feasible object dynamics.
We test this hypothesis by incorporating a physical simulator
into a least-squares estimation procedure. We initialise a full
trajectory estimate using RANSAC followed by gradient descent
refinement. We present results for 2D image sequences
consisting of single ambiguous, visible or occluded balls, as well
as results for 3D computer-generated sequences of objects in
free-flight with added noise. Results suggest that restricting the
estimation to allow only motions that are feasible according
to the physics simulator can produce marked improvement
when the observed object motion is within the limits of the
physics simulator and its world model. Conversely, merely
penalising deviations from feasible physical dynamics produces
a consistent but incremental improvement over more common
pdf (1.63 MB)