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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Event-Based Motion Segmentation by Motion Compensation
Organization Unit
Authors
  • Timo Stoffregen
  • Guillermo Gallego
  • Tom Drummond
  • Lindsay Kleeman
  • Davide Scaramuzza
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISBN 978-1-7281-4803-8
Page Range 7243 - 7252
Event Title 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Event Type conference
Event Location Seoul, Korea (South)
Event Start Date November 27 - 2019
Event End Date December 2 - 2019
Publisher IEEE
Abstract Text 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.
Digital Object Identifier 10.1109/iccv.2019.00734
Other Identification Number merlin-id:20299
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