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Long-term tracking in the wild: a benchmark

Abstract:

We introduce the OxUvA dataset and benchmark for evaluating single-object tracking algorithms. Benchmarks have enabled great strides in the field of object tracking by defining standardized evaluations on large sets of diverse videos. However, these works have focused exclusively on sequences that are just tens of seconds in length and in which the target is always visible. Consequently, most researchers have designed methods tailored to this “short-term” scenario, which is poorly representat...

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Publication status:
Published
Peer review status:
Peer reviewed

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Files:
  • (Accepted manuscript, pdf, 817.7KB)
Publisher copy:
10.1007/978-3-030-01219-9_41

Authors


More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
New College
Role:
Author
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Publisher:
Springer, Cham Publisher's website
Journal:
ECCV 2018: Computer Vision – ECCV 2018 Journal website
Volume:
11207
Pages:
692-707
Series:
Lecture Notes in Computer Science
Host title:
ECCV 2018: Computer Vision – ECCV 2018
Publication date:
2018-10-07
Acceptance date:
2018-07-03
DOI:
ISSN:
0302-9743
Source identifiers:
940498
ISBN:
9783030012182
Pubs id:
pubs:940498
UUID:
uuid:23a10a1c-a4dd-405a-bf9c-b7838b15c9f8
Local pid:
pubs:940498
Deposit date:
2018-11-13

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