GAN-based Pose-aware Regulation for Video-based Person Re-identification
| Autor: | Alessandro Borgia, Yang Hua, Elyor Kodirov, Neil Robertson |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Matching (statistics)
Exploit Computer science business.industry Feature extraction Context (language use) Pattern recognition 02 engineering and technology 010501 environmental sciences 01 natural sciences Recurrent neural network Discriminative model 0202 electrical engineering electronic engineering information engineering Benchmark (computing) 020201 artificial intelligence & image processing Artificial intelligence business 0105 earth and related environmental sciences Complement (set theory) |
| Zdroj: | Borgia, A, Hua, Y, Kodirov, E & Robertson, N M 2019, GAN-based Pose-aware Regulation for Video-based Person Re-identification . in WACV 2019: The IEEE Winter Conference on Applications of Computer Vision: Proceedings . IEEE Winter Conference on Applications of Computer Vision (WACV): Proceedings . https://doi.org/10.1109/WACV.2019.00130 WACV |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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