GAN-based Pose-aware Regulation for Video-based Person Re-identification

Alessandro Borgia, Yang Hua, Elyor Kodirov, Neil M. Robertson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
320 Downloads (Pure)


Video-based person re-identification deals with the inherent difficulty of matching unregulated sequences with different length and with incomplete target pose/viewpoint structure. Common approaches operate either by reducing the problem to the still images case, facing a significant information loss, or by exploiting inter-sequence temporal dependencies as in Siamese Recurrent Neural Networks or in gait analysis. However, in all cases, the intersequences pose/viewpoint misalignment is not considered, and the existing spatial approaches are mostly limited to the still images context. To this end, we propose a novel approach that can exploit more effectively the rich video information, by accounting for the role that the changing pose/viewpoint factor plays in the sequences matching process. Specifically, our approach consists of two components. The first one attempts to complement the original pose-incomplete information carried by the sequences with synthetic GAN-generated images, and fuse their feature vectors into a more discriminative viewpointinsensitive embedding, namely Weighted Fusion (WF). Another one performs an explicit pose-based alignment of sequence pairs to promote coherent feature matching, namely Weighted-Pose Regulation (WPR). Extensive experiments on two large video-based benchmark datasets show that our approach outperforms considerably existing methods.
Original languageEnglish
Title of host publicationWACV 2019: The IEEE Winter Conference on Applications of Computer Vision: Proceedings
Number of pages10
ISBN (Electronic)978-1-7281-1975-5
Publication statusPublished - 07 Mar 2019

Publication series

NameIEEE Winter Conference on Applications of Computer Vision (WACV): Proceedings
ISSN (Print)1550-5790


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