Person Search in Videos with One Portrait Through Visual and Temporal Links
Refereed conference paper presented and published in conference proceedings


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AbstractIn 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 ).
Acceptance Date12/07/2018
All Author(s) ListQingqiu Wang, Wentao Liu, Dahua Lin
Name of Conference15th European Conference on Computer Vision, ECCV 2018
Start Date of Conference08/09/2018
End Date of Conference14/09/2018
Place of ConferenceMunich, Germany
Country/Region of ConferenceGermany
Proceedings TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Year2018
Month9
Volume Number11217
PublisherSpringer
Pages437 - 454
ISBN978-303001260-1
ISSN03029743
LanguagesEnglish-United States

Last updated on 2021-25-02 at 02:54