# Improving Person Re-Identification with Temporal Constraints

arXiv: https://arxiv.org/abs/2111.09056 · HTML: https://mlsanity.com/paper/2111.09056/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 33 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 19 of 19)

- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Beyond Part Models: Person Retrieval with Refined Part Pooling (and a Strong Convolutional Baseline)](https://mlsanity.com/paper/1711.09349/) (2017)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [Re-ranking Person Re-identification with k-reciprocal Encoding](https://mlsanity.com/paper/1701.08398/) (2017)
- [Harmonious Attention Network for Person Re-Identification](https://mlsanity.com/paper/1802.08122/) (2018)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [A Strong Baseline and Batch Normalization Neck for Deep Person Re-identification](https://mlsanity.com/paper/1906.08332/) (2019)
- [Finding Tiny Faces](https://mlsanity.com/paper/1612.04402/) (2016)
- [Multi-Level Factorisation Net for Person Re-Identification](https://mlsanity.com/paper/1803.09132/) (2018)
- [Spatial-Temporal Person Re-identification](https://mlsanity.com/paper/1812.03282/) (2018)
- [Multi-scale Deep Learning Architectures for Person Re-identification](https://mlsanity.com/paper/1709.05165/) (2017)
- [Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation](https://mlsanity.com/paper/1909.09675/) (2019)
- [Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch](https://mlsanity.com/paper/1910.10093/) (2019)
- [The Devil is in the Middle: Exploiting Mid-level Representations for Cross-Domain Instance Matching](https://mlsanity.com/paper/1711.08106/) (2017)
- [Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatial-Temporal Patterns](https://mlsanity.com/paper/1803.07293/) (2018)
- [How important are faces for person re-identification?](https://mlsanity.com/paper/2010.06307/) (2020)
- [Joint Person Re-identification and Camera Network Topology Inference in Multiple Cameras](https://mlsanity.com/paper/1710.00983/) (2017)
- [Weakly Supervised Person Re-Identification](https://mlsanity.com/paper/1904.03832/) (2019)

## Built on by (most cited first, 3 of 3)

- [Learnable Privacy-Preserving Anonymization for Pedestrian Images](https://mlsanity.com/paper/2207.11677/) (2022)
- [Segmentation Enhanced Lameness Detection in Dairy Cows from RGB and Depth Video](https://mlsanity.com/paper/2206.04449/) (2022)
- [A Dual-stage Prompt-driven Privacy-preserving Paradigm for Person Re-Identification](https://mlsanity.com/paper/2511.05092/) (2025)

## Abstract (arXiv)

In this paper we introduce an image-based person re-identification dataset collected across five non-overlapping camera views in the large and busy airport in Dublin, Ireland. Unlike all publicly available image-based datasets, our dataset contains timestamp information in addition to frame number, and camera and person IDs. Also our dataset has been fully anonymized to comply with modern data privacy regulations. We apply state-of-the-art person re-identification models to our dataset and show that by leveraging the available timestamp information we are able to achieve a significant gain of 37.43% in mAP and a gain of 30.22% in Rank1 accuracy. We also propose a Bayesian temporal re-ranking post-processing step, which further adds a 10.03% gain in mAP and 9.95% gain in Rank1 accuracy metrics. This work on combining visual and temporal information is not possible on other image-based person re-identification datasets. We believe that the proposed new dataset will enable further development of person re-identification research for challenging real-world applications. DAA dataset can be downloaded from https://bit.ly/3AtXTd6