Conference item
Long-term tracking in the wild: a benchmark
- Abstract:
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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:
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(Accepted manuscript, pdf, 817.7KB)
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- Publisher copy:
- 10.1007/978-3-030-01219-9_41
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Authors
Funding
Bibliographic Details
- 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:
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0302-9743
- Source identifiers:
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940498
- ISBN:
- 9783030012182
Item Description
- Pubs id:
-
pubs:940498
- UUID:
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uuid:23a10a1c-a4dd-405a-bf9c-b7838b15c9f8
- Local pid:
- pubs:940498
- Deposit date:
- 2018-11-13
Terms of use
- Copyright holder:
- Springer Nature Switzerland AG
- Copyright date:
- 2018
- Notes:
- Copyright © 2018 Springer Nature Switzerland AG. This is the accepted manuscript version of the paper. The final version is available online from Springer at: https://doi.org/10.1007/978-3-030-01219-9_41
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