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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title End-to-End Learning of Representations for Asynchronous Event-Based Data
Organization Unit
Authors
  • Daniel Gehrig
  • Antonio Loquercio
  • Konstantinos Derpanis
  • 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 5632 - 5642
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 Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events”. They have appealing advantages over frame based cameras for computer vision, including high temporal resolution, high dynamic range, and no motion blur. Due to the sparse, non-uniform spatio-temporal layout of the event signal, pattern recognition algorithms typically aggregate events into a grid-based representation and subsequently process it by a standard vision pipeline, e.g., Convolutional Neural Network (CNN). In this work, we introduce a general framework to convert event streams into grid-based representations by means of strictly differentiable operations. Our framework comes with two main advantages: (i) allows learning the input event representation together with the task dedicated network in an end to end manner, and (ii) lays out a taxonomy that unifies the majority of extant event representations in the literature and identifies novel ones. Empirically, we show that our approach to learning the event representation end-to-end yields an improvement of approximately 12% on optical flow estimation and object recognition over state-of-the-art methods.
Digital Object Identifier 10.1109/iccv.2019.00573
Other Identification Number merlin-id:20298
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