Long-term tracking in the wild: a benchmark

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 a...

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Main Authors: Valmadre, J, Bertinetto, L, Henriques, J, Tao, R, Vedaldi, A, Smeulders, A, Torr, P, Gavves, E
Format: Conference item
Published: Springer, Cham 2018
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author Valmadre, J
Bertinetto, L
Henriques, J
Tao, R
Vedaldi, A
Smeulders, A
Torr, P
Gavves, E
author_facet Valmadre, J
Bertinetto, L
Henriques, J
Tao, R
Vedaldi, A
Smeulders, A
Torr, P
Gavves, E
author_sort Valmadre, J
collection OXFORD
description 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 representative of practitioners’ needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 h of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used “in the wild”. The project website is oxuva.net.
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spelling oxford-uuid:23a10a1c-a4dd-405a-bf9c-b7838b15c9f82022-03-26T11:45:19ZLong-term tracking in the wild: a benchmarkConference itemhttp://purl.org/coar/resource_type/c_5794uuid:23a10a1c-a4dd-405a-bf9c-b7838b15c9f8Symplectic Elements at OxfordSpringer, Cham2018Valmadre, JBertinetto, LHenriques, JTao, RVedaldi, ASmeulders, ATorr, PGavves, EWe 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 representative of practitioners’ needs. Aiming to address this disparity, we compile a long-term, large-scale tracking dataset of sequences with average length greater than two minutes and with frequent target object disappearance. The OxUvA dataset is much larger than the object tracking datasets of recent years: it comprises 366 sequences spanning 14 h of video. We assess the performance of several algorithms, considering both the ability to locate the target and to determine whether it is present or absent. Our goal is to offer the community a large and diverse benchmark to enable the design and evaluation of tracking methods ready to be used “in the wild”. The project website is oxuva.net.
spellingShingle Valmadre, J
Bertinetto, L
Henriques, J
Tao, R
Vedaldi, A
Smeulders, A
Torr, P
Gavves, E
Long-term tracking in the wild: a benchmark
title Long-term tracking in the wild: a benchmark
title_full Long-term tracking in the wild: a benchmark
title_fullStr Long-term tracking in the wild: a benchmark
title_full_unstemmed Long-term tracking in the wild: a benchmark
title_short Long-term tracking in the wild: a benchmark
title_sort long term tracking in the wild a benchmark
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