  Among prerequisites for a synthetic agent to interact with dynamic scenes,
the ability to identify independently moving objects is specifically important.
From an application perspective, nevertheless, standard cameras may deteriorate
remarkably under aggressive motion and challenging illumination conditions. In
contrast, event-based cameras, as a category of novel biologically inspired
sensors, deliver advantages to deal with these challenges. Its rapid response
and asynchronous nature enables it to capture visual stimuli at exactly the
same rate of the scene dynamics. In this paper, we present a cascaded two-level
multi-model fitting method for identifying independently moving objects (i.e.,
the motion segmentation problem) with a monocular event camera. The first level
leverages tracking of event features and solves the feature clustering problem
under a progressive multi-model fitting scheme. Initialized with the resulting
motion model instances, the second level further addresses the event clustering
problem using a spatio-temporal graph-cut method. This combination leads to
efficient and accurate event-wise motion segmentation that cannot be achieved
by any of them alone. Experiments demonstrate the effectiveness and versatility
of our method in real-world scenes with different motion patterns and an
unknown number of independently moving objects.
