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An End-to-End Foreground-Aware Network for Person Re-Identification by Yiheng Liu; Wengang Zhou; Jianzhuang Liu; Guo-Jun Qi; Qi Tian; Houqiang Li is a Computer Science article available to read on EtoBox.

What is An End-to-End Foreground-Aware Network for Person Re-Identification about?

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 mode

Who reads An End-to-End Foreground-Aware Network for Person Re-Identification?

Author

Yiheng Liu; Wengang Zhou; Jianzhuang Liu; Guo-Jun Qi; Qi Tian; Houqiang Li

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)