PETS2021:穿透叶探测和跟踪挑战与评估

Luis Patino, Jonathan N. Boyle, J. Ferryman, Jonas Auer, Julian Pegoraro, R. Pflugfelder, Mertcan Cokbas, J. Konrad, P. Ishwar, Giulia Slavic, L. Marcenaro, Yifan Jiang, Youngsaeng Jin, Hanseok Ko, Guangliang Zhao, Guy Ben-Yosef, Jianwei Qiu
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引用次数: 2

摘要

本文介绍了由欧盟FOLDOUT项目赞助的与AVSS2021一起举行的PETS2021挑战的结果。挑战包括发布一个关于树叶检测的新型视频监控数据集,解决碎片遮挡场景中人员检测和跟踪的定义挑战,以及对六个全球参与者提交的挑战结果进行定量和定性的性能评估。结果表明,虽然几种检测和跟踪方法总体上取得了良好的效果,但对于监视系统来说,穿透叶检测和跟踪仍然是一项具有挑战性的任务,特别是当它作为行为(威胁)识别的输入时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PETS2021: Through-foliage detection and tracking challenge and evaluation
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.
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