The visual object tracking VOT2015 challenge results
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to...
| Main Authors: | , , , , , , , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
IEEE
2016
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| _version_ | 1826313741648527360 |
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| author | Kristan, M Matas, J Leonardis, A Bibi, A Bertinetto, L Miksik, O Torr, PHS Hicks, SL Golodetz, S |
| author_facet | Kristan, M Matas, J Leonardis, A Bibi, A Bertinetto, L Miksik, O Torr, PHS Hicks, SL Golodetz, S |
| author_sort | Kristan, M |
| collection | OXFORD |
| description | The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website. |
| first_indexed | 2024-09-25T04:19:41Z |
| format | Conference item |
| id | oxford-uuid:f42b1cdf-e3b6-4aad-8cc9-caee09d378f9 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:19:41Z |
| publishDate | 2016 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:f42b1cdf-e3b6-4aad-8cc9-caee09d378f92024-07-30T16:19:58ZThe visual object tracking VOT2015 challenge resultsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:f42b1cdf-e3b6-4aad-8cc9-caee09d378f9EnglishSymplectic ElementsIEEE2016Kristan, MMatas, JLeonardis, ABibi, ABertinetto, LMiksik, OTorr, PHSHicks, SLGolodetz, SThe Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date. For each participating tracker, a short description is provided in the appendix. Features of the VOT2015 challenge that go beyond its VOT2014 predecessor are: (i) a new VOT2015 dataset twice as large as in VOT2014 with full annotation of targets by rotated bounding boxes and per-frame attribute, (ii) extensions of the VOT2014 evaluation methodology by introduction of a new performance measure. The dataset, the evaluation kit as well as the results are publicly available at the challenge website. |
| spellingShingle | Kristan, M Matas, J Leonardis, A Bibi, A Bertinetto, L Miksik, O Torr, PHS Hicks, SL Golodetz, S The visual object tracking VOT2015 challenge results |
| title | The visual object tracking VOT2015 challenge results |
| title_full | The visual object tracking VOT2015 challenge results |
| title_fullStr | The visual object tracking VOT2015 challenge results |
| title_full_unstemmed | The visual object tracking VOT2015 challenge results |
| title_short | The visual object tracking VOT2015 challenge results |
| title_sort | visual object tracking vot2015 challenge results |
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