  Both a good understanding of geometrical concepts and a broad familiarity
with objects lead to our excellent perception of moving objects. The human
ability to detect and segment moving objects works in the presence of multiple
objects, complex background geometry, motion of the observer and even
camouflage. How humans perceive moving objects so reliably is a longstanding
research question in computer vision and borrows findings from related areas
such as psychology, cognitive science and physics. One approach to the problem
is to teach a deep network to model all of these effects. This contrasts with
the strategy used by human vision, where cognitive processes and body design
are tightly coupled and each is responsible for certain aspects of correctly
identifying moving objects. Similarly from the computer vision perspective,
there is evidence that classical, geometry-based techniques are better suited
to the "motion-based" parts of the problem, while deep networks are more
suitable for modeling appearance. In this work, we argue that the coupling of
camera rotation and camera translation can create complex motion fields that
are difficult for a deep network to untangle directly. We present a novel
probabilistic model to estimate the camera's rotation given the motion field.
We then rectify the flow field to obtain a rotation-compensated motion field
for subsequent segmentation. This strategy of first estimating camera motion,
and then allowing a network to learn the remaining parts of the problem, yields
improved results on the widely used DAVIS benchmark as well as the recently
published motion segmentation data set MoCA (Moving Camouflaged Animals).
