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• W2884913105 abstract "In real-world applications, e.g. law enforcement and video retrieval, one often needs to search a certain person in long videos with just one portrait. This is much more challenging than the conventional settings for person re-identification, as the search may need to be carried out in the environments different from where the portrait was taken. In this paper, we aim to tackle this challenge and propose a novel framework, which takes into account the identity invariance along a tracklet, thus allowing person identities to be propagated via both the visual and the temporal links. We also develop a novel scheme called Progressive Propagation via Competitive Consensus, which significantly improves the reliability of the propagation process. To promote the study of person search, we construct a large-scale benchmark, which contains 127K manually annotated tracklets from 192 movies. Experiments show that our approach remarkably outperforms mainstream person re-id methods, raising the mAP from $$42.16%$$ to $$62.27%$$ (Code at https://github.com/hqqasw/person-search-PPCC )." @default.
• W2884913105 created "2018-08-03" @default.
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• W2884913105 date "2018-01-01" @default.
• W2884913105 modified "2023-10-03" @default.
• W2884913105 title "Person Search in Videos with One Portrait Through Visual and Temporal Links" @default.
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• W2884913105 doi "https://doi.org/10.1007/978-3-030-01261-8_26" @default.
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