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An End-to-End Foreground-Aware Network for Person Re-Identification.

Authors :
Liu, Yiheng
Zhou, Wengang
Liu, Jianzhuang
Qi, Guo-Jun
Tian, Qi
Li, Houqiang
Source :
IEEE Transactions on Image Processing. 2021, Vol. 30, p2060-2071. 12p.
Publication Year :
2021

Abstract

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10577149
Volume :
30
Database :
Academic Search Index
Journal :
IEEE Transactions on Image Processing
Publication Type :
Academic Journal
Accession number :
170077643
Full Text :
https://doi.org/10.1109/TIP.2021.3050839