  Segmentation of moving objects in dynamic scenes is a key process in scene
understanding for navigation tasks. Classical cameras suffer from motion blur
in such scenarios rendering them effete. On the contrary, event cameras,
because of their high temporal resolution and lack of motion blur, are
tailor-made for this problem. We present an approach for monocular multi-motion
segmentation, which combines bottom-up feature tracking and top-down motion
compensation into a unified pipeline, which is the first of its kind to our
knowledge. Using the events within a time-interval, our method segments the
scene into multiple motions by splitting and merging. We further speed up our
method by using the concept of motion propagation and cluster keyslices.
  The approach was successfully evaluated on both challenging real-world and
synthetic scenarios from the EV-IMO, EED, and MOD datasets and outperformed the
state-of-the-art detection rate by 12\%, achieving a new state-of-the-art
average detection rate of 81.06%, 94.2% and 82.35% on the aforementioned
datasets. To enable further research and systematic evaluation of multi-motion
segmentation, we present and open-source a new dataset/benchmark called MOD++,
which includes challenging sequences and extensive data stratification in-terms
of camera and object motion, velocity magnitudes, direction, and rotational
speeds.

Computer Vision and Pattern Recognition