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Detect to track and track to detect

Abstract:

Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-fra...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1109/ICCV.2017.330

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Oxford college:
Brasenose College
Role:
Author
ORCID:
0000-0002-8945-8573
More from this funder
Funding agency for:
Feichtenhofer, C
Publisher:
Institute of Electrical and Electronics Engineers Publisher's website
Journal:
IEEE International Conference on Computer Vision 2017 Journal website
Pages:
3057-3065
Host title:
IEEE International Conference on Computer Vision 2017
Publication date:
2017-12-25
Acceptance date:
2017-11-08
DOI:
ISSN:
978-1-5386-1033-6 and 1550-5499
Source identifiers:
821116
ISBN:
9781538610329
Pubs id:
pubs:821116
UUID:
uuid:638773c5-b132-4d89-ad99-1ac49a3c878c
Local pid:
pubs:821116
Deposit date:
2018-08-17

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