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

arXiv: https://arxiv.org/abs/1910.11547 · HTML: https://mlsanity.com/paper/1910.11547/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [AAformer: Auto-Aligned Transformer for Person Re-Identification](https://mlsanity.com/paper/2104.00921/) (2021)
- [TOPIC: A Parallel Association Paradigm for Multi-Object Tracking under Complex Motions and Diverse Scenes](https://mlsanity.com/paper/2308.11157/) (2023)
- [Person Re-identification: A Retrospective on Domain Specific Open Challenges and Future Trends](https://mlsanity.com/paper/2202.13121/) (2022)
- [PersonMAE: Person Re-Identification Pre-Training with Masked AutoEncoders](https://mlsanity.com/paper/2311.04496/) (2023)
- [Improving CNN-based Person Re-identification using score Normalization](https://mlsanity.com/paper/2307.00397/) (2023)
- [Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling](https://mlsanity.com/paper/2110.15610/) (2021)
- [Background Matters Too: A Language-Enhanced Adversarial Framework for Person Re-Identification](https://mlsanity.com/paper/2509.03032/) (2025)
- [End-to-End Context-Aided Unicity Matching for Person Re-identification](https://mlsanity.com/paper/2210.12008/) (2022)

## Abstract (arXiv)

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. In 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. To this end, we propose an end-to-end foreground-aware network to discriminate foreground from background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for 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 the backgrounds, which greatly reduces the negative impacts of changing backgrounds on matching an identical across different camera views. Notably, in contrast to existing methods, our approach does not require any 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.