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

香港中文大學研究人員
替代計量分析
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其它資訊
摘要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 ).
出版社接受日期12.07.2018
著者Qingqiu Wang, Wentao Liu, Dahua Lin
會議名稱15th European Conference on Computer Vision, ECCV 2018
會議開始日08.09.2018
會議完結日14.09.2018
會議地點Munich, Germany
會議國家/地區德國
會議論文集題名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年份2018
月份9
卷號11217
出版社Springer
頁次437 - 454
國際標準書號978-303001260-1
國際標準期刊號03029743
語言美式英語

上次更新時間 2021-22-01 於 01:54