Simultaneous Optical Flow and Intensity Estimation from an Event Camera

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Title: Simultaneous Optical Flow and Intensity Estimation from an Event Camera
Authors: Bardow, P
Davison, AJ
Leutenegger, S
Item Type: Conference Paper
Abstract: Event cameras are bio-inspired vision sensors which mimic retinas to measure per-pixel intensity change rather than outputting an actual intensity image. This proposed paradigm shift away from traditional frame cameras offers significant potential advantages: namely avoiding high data rates, dynamic range limitations and motion blur. Unfortunately, however, established computer vision algorithms may not at all be applied directly to event cameras. Methods proposed so far to reconstruct images, estimate optical flow, track a camera and reconstruct a scene come with severe restrictions on the environment or on the motion of the camera, e.g. allowing only rotation. Here, we propose, to the best of our knowledge, the first algorithm to simultaneously recover the motion field and brightness image, while the camera undergoes a generic motion through any scene. Our approach employs minimisation of a cost function that contains the asynchronous event data as well as spatial and temporal regularisation within a sliding window time interval. Our implementation relies on GPU optimisation and runs in near real-time. In a series of examples, we demonstrate the successful operation of our framework, including in situations where conventional cameras suffer from dynamic range limitations and motion blur.
Issue Date: 12-Dec-2016
Date of Acceptance: 11-Apr-2016
ISSN: 1063-6919
Publisher: Computer Vision Foundation (CVF)
Journal / Book Title: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Copyright Statement: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor/Funder: Dyson Technology Limited
Funder's Grant Number: PO 4500378543
Conference Name: Computer Vision and Pattern Recognition 2016
Keywords: Science & Technology
Computer Science, Artificial Intelligence
Computer Science
Publication Status: Published
Start Date: 2016-06-26
Finish Date: 2016-07-01
Conference Place: Las Vegas, Nevada USA
Appears in Collections:Faculty of Engineering

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