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Long-Term Tracking in the Wild: A Benchmark

  • Jack Valmadre
  • Luca Bertinetto
  • João F. Henriques
  • Ran Tao
  • Andrea Vedaldi
  • Arnold W. M. Smeulders
  • Philip H. S. Torr
  • Efstratios Gavves
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11207)

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 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.

Supplementary material

474178_1_En_41_MOESM1_ESM.pdf (265 kb)
Supplementary material 1 (pdf 265 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jack Valmadre
    • 1
  • Luca Bertinetto
    • 1
  • João F. Henriques
    • 1
  • Ran Tao
    • 2
  • Andrea Vedaldi
    • 1
  • Arnold W. M. Smeulders
    • 2
  • Philip H. S. Torr
    • 1
  • Efstratios Gavves
    • 2
  1. 1.University of OxfordOxfordUK
  2. 2.University of AmsterdamAmsterdamThe Netherlands

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