Event-Based Motion Segmentation by Motion Compensation
release_5e2wpc4bnzbjvc4gism2bnhp7e
by
Timo Stoffregen and Guillermo Gallego and Tom Drummond and Lindsay
Kleeman and Davide Scaramuzza
2019
Abstract
In contrast to traditional cameras, whose pixels have a common exposure time,
event-based cameras are novel bio-inspired sensors whose pixels work
independently and asynchronously output intensity changes (called "events"),
with microsecond resolution. Since events are caused by the apparent motion of
objects, event-based cameras sample visual information based on the scene
dynamics and are, therefore, a more natural fit than traditional cameras to
acquire motion, especially at high speeds, where traditional cameras suffer
from motion blur. However, distinguishing between events caused by different
moving objects and by the camera's ego-motion is a challenging task. We present
the first per-event segmentation method for splitting a scene into
independently moving objects. Our method jointly estimates the event-object
associations (i.e., segmentation) and the motion parameters of the objects (or
the background) by maximization of an objective function, which builds upon
recent results on event-based motion-compensation. We provide a thorough
evaluation of our method on a public dataset, outperforming the
state-of-the-art by as much as 10%. We also show the first quantitative
evaluation of a segmentation algorithm for event cameras, yielding around 90%
accuracy at 4 pixels relative displacement.
In text/plain
format
Archived Files and Locations
application/pdf 16.8 MB
file_3krva3jw3vhzlah65g5opmn63q
|
arxiv.org (repository) web.archive.org (webarchive) |
1904.01293v1
access all versions, variants, and formats of this works (eg, pre-prints)