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dc.contributor.author
Barranco, Francisco
dc.contributor.author
Fermüller, Cornelia
dc.contributor.author
Aloimonos, Yiannis
dc.contributor.author
Delbrück, Tobias
dc.date.accessioned
2019-05-29T11:25:22Z
dc.date.available
2017-06-12T02:13:05Z
dc.date.available
2019-05-29T11:19:52Z
dc.date.available
2019-05-29T11:25:22Z
dc.date.issued
2016-02-23
dc.identifier.issn
1662-453X
dc.identifier.issn
1662-4548
dc.identifier.other
10.3389/fnins.2016.00049
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/113863
dc.identifier.doi
10.3929/ethz-b-000113863
dc.description.abstract
Standardized benchmarks in Computer Vision have greatly contributed to the advance of approaches to many problems in the field. If we want to enhance the visibility of event-driven vision and increase its impact, we will need benchmarks that allow comparison among different neuromorphic methods as well as comparison to Computer Vision conventional approaches. We present datasets to evaluate the accuracy of frame-free and frame-based approaches for tasks of visual navigation. Similar to conventional Computer Vision datasets, we provide synthetic and real scenes, with the synthetic data created with graphics packages, and the real data recorded using a mobile robotic platform carrying a dynamic and active pixel vision sensor (DAVIS) and an RGB+Depth sensor. For both datasets the cameras move with a rigid motion in a static scene, and the data includes the images, events, optic flow, 3D camera motion, and the depth of the scene, along with calibration procedures. Finally, we also provide simulated event data generated synthetically from well-known frame-based optical flow datasets.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Event-driven methods
en_US
dc.subject
Frame-free sensors
en_US
dc.subject
Visual navigation
en_US
dc.subject
Dataset
en_US
dc.subject
Calibration
en_US
dc.title
A Dataset for Visual Navigation with Neuromorphic Methods
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Frontiers in Neuroscience
ethz.journal.volume
10
en_US
ethz.journal.abbreviated
Front Neurosci
ethz.pages.start
49
en_US
ethz.size
9 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
009497874
ethz.publication.place
Lausanne
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2017-06-12T02:18:03Z
ethz.source
ECIT
ethz.identifier.importid
imp59365430a554176712
ethz.ecitpid
pub:175608
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T20:15:33Z
ethz.rosetta.lastUpdated
2019-05-29T11:25:36Z
ethz.rosetta.versionExported
true
ethz.COinS
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