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PETS2021: Through-foliage detection and tracking challenge and evaluation

Patino, L., Boyle, J., Ferryman, J., Auer, J., Pegoraro, J., Pflugfelder, R., Cokbas, M., Janusz, K., Ishwar, P., Slavic, G., Marcenaro, L., Jiang, Y., Jin, Y., Ko, H., Zhao, G., Ben-yosef, G. and Qiu, J. (2022) PETS2021: Through-foliage detection and tracking challenge and evaluation. In: 17th IEEE Int'l Conf on Advanced Video and Signal-based Surveillance (AVSS 2021), 16-19 NOV 2021, Virtual,

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To link to this item DOI: 10.1109/AVSS52988.2021.9663837


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.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:104305

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