PETS2021: Through-foliage detection and tracking challenge and evaluation

Luis Patino, Jonathan Boyle, James Ferryman, Jonas Auer, Julian Pegoraro, Roman Pflugfelder, Mertcan Cokbas, Janusz Konrad, Prakash Ishwar, Giulia Slavic, Lucio Marcenaro, Yifan Jiang, Youngsaeng Jin, Hanseok Ko, Guangliang Zhao, Guy Ben-Yosef, Jianwei Qiu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

This paper presents the outcomes of the PETS2021 challenge held in conjunction with AVSS2021 and sponsored by the EU FOLDOUT project. The challenge comprises the publication of a novel video surveillance dataset on through-foliage detection, the defined challenges addressing person detection and tracking in fragmented occlusion scenarios, and quantitative and qualitative performance evaluation of challenge results submitted by six worldwide participants. The results show that while several detection and tracking methods achieve overall good results, through-foliage detection and tracking remains a challenging task for surveillance systems especially as it serves as the input to behaviour (threat) recognition.

Original languageEnglish
Title of host publicationAVSS 2021 - 17th IEEE International Conference on Advanced Video and Signal-Based Surveillance
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665433969
DOIs
Publication statusPublished - 2021
Event17th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2021 - Virtual, Online, United States
Duration: 2021 Nov 162021 Nov 19

Publication series

NameAVSS 2021 - 17th IEEE International Conference on Advanced Video and Signal-Based Surveillance

Conference

Conference17th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2021
Country/TerritoryUnited States
CityVirtual, Online
Period21/11/1621/11/19

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Media Technology

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