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The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results

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  • معلومة اضافية
    • بيانات النشر:
      Linköpings universitet, Datorseende
      Linköpings universitet, Tekniska fakulteten
      Linköpings universitet, Centrum för medicinsk bildvetenskap och visualisering, CMIV
      University of Ljubljana, Slovenia
      Czech Technical University, Czech Republic
      University of Birmingham, England
      Austrian Institute Technology, Austria
      Termisk Syst Tekn AB, Linkoping, Sweden
      Parthenope University of Naples, Italy
      University of Autonoma Madrid, Spain
      University of Ottawa, Canada
      Kyiv Polytech Institute, Ukraine
      Hacettepe University, Turkey
      POSTECH, South Korea
      University of Albany, GA USA
      Australian National University, Australia; Chinese Academic Science, Peoples R China
      ARC Centre Excellence Robot Vis, Australia; CSIRO, Australia
      University of Missouri, MO 65211 USA
      University of Missouri, Columbia, USA
      US Navy, DC 20375 USA
      ARC Centre Excellence Robot Vis, Australia
      Graz University of Technology, Austria
      NAVER Corp, South Korea
      University of Oxford, England
      Zhejiang University, Peoples R China
      University of Surrey, England
      Imperial Coll London, England
      Fraunhofer IOSB, Germany
      Harbin Institute Technology, Peoples R China
      Lehigh University, PA 18015 USA
      SPRINGER INT PUBLISHING AG
    • الموضوع:
      2016
    • Collection:
      Linköping University Electronic Press (LiU E-Press)
    • نبذة مختصرة :
      The Thermal Infrared Visual Object Tracking challenge 2016, VOT-TIR2016, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2016 is the second benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2016 challenge is similar to the 2015 challenge, the main difference is the introduction of new, more difficult sequences into the dataset. Furthermore, VOT-TIR2016 evaluation adopted the improvements regarding overlap calculation in VOT2016. Compared to VOT-TIR2015, a significant general improvement of results has been observed, which partly compensate for the more difficult sequences. The dataset, the evaluation kit, as well as the results are publicly available at the challenge website.
    • File Description:
      application/pdf
    • ISBN:
      978-3-319-48881-3
      978-3-319-48880-6
      978-0-00-389501-8
      3-319-48881-3
      3-319-48880-5
      0-00-389501-7
    • Relation:
      Lecture Notes in Computer Science, 0302-9743; 9914; Computer Vision – ECCV 2016 Workshops. ECCV 2016., p. 824-849; orcid:0000-0002-6096-3648; orcid:0000-0001-6199-9362; orcid:0000-0002-6591-9400; orcid:0000-0003-3292-7153; orcid:0000-0002-6763-5487; orcid:0000-0001-6144-9520; http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133773; urn:isbn:978-3-319-48881-3; urn:isbn:978-3-319-48880-6; ISI:000389501700055
    • الرقم المعرف:
      10.1007/978-3-319-48881-3_55
    • الدخول الالكتروني :
      http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-133773
      https://doi.org/10.1007/978-3-319-48881-3_55
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.809C05B