# Single Camera Training for Person Re-identification

arXiv: https://arxiv.org/abs/1909.10848 · HTML: https://mlsanity.com/paper/1909.10848/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2019.

## Built on by (most cited first, 9 of 9)

- [Spatiotemporal Transformer for Video-based Person Re-identification](https://mlsanity.com/paper/2103.16469/) (2021)
- [Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes](https://mlsanity.com/paper/2107.13904/) (2021)
- [Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification](https://mlsanity.com/paper/2203.15210/) (2022)
- [ProtoHPE: Prototype-guided High-frequency Patch Enhancement for Visible-Infrared Person Re-identification](https://mlsanity.com/paper/2310.07552/) (2023)
- [Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking](https://mlsanity.com/paper/2112.07664/) (2021)
- [Adversarial Pairwise Reverse Attention for Camera Performance Imbalance in Person Re-identification: New Dataset and Metrics](https://mlsanity.com/paper/2207.01204/) (2022)
- [Domain-adaptive Person Re-identification without Cross-camera Paired Samples](https://mlsanity.com/paper/2307.06533/) (2023)
- [Learning Intra and Inter-Camera Invariance for Isolated Camera Supervised Person Re-identification](https://mlsanity.com/paper/2311.01155/) (2023)
- [Camera-Invariant Meta-Learning Network for Single-Camera-Training Person Re-identification](https://mlsanity.com/paper/2406.14797/) (2024)

## Abstract (arXiv)

Person re-identification (ReID) aims at finding the same person in different cameras. Training such systems usually requires a large amount of cross-camera pedestrians to be annotated from surveillance videos, which is labor-consuming especially when the number of cameras is large. Differently, this paper investigates ReID in an unexplored single-camera-training (SCT) setting, where each person in the training set appears in only one camera. To the best of our knowledge, this setting was never studied before. SCT enjoys the advantage of low-cost data collection and annotation, and thus eases ReID systems to be trained in a brand new environment. However, it raises major challenges due to the lack of cross-camera person occurrences, which conventional approaches heavily rely on to extract discriminative features. The key to dealing with the challenges in the SCT setting lies in designing an effective mechanism to complement cross-camera annotation. We start with a regular deep network for feature extraction, upon which we propose a novel loss function named multi-camera negative loss (MCNL). This is a metric learning loss motivated by probability, suggesting that in a multi-camera system, one image is more likely to be closer to the most similar negative sample in other cameras than to the most similar negative sample in the same camera. In experiments, MCNL significantly boosts ReID accuracy in the SCT setting, which paves the way of fast deployment of ReID systems with good performance on new target scenes.