# Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization

arXiv: https://arxiv.org/abs/1910.11560 · HTML: https://mlsanity.com/paper/1910.11560/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2019.

## Built on by (most cited first, 6 of 6)

- [MGH: Metadata Guided Hypergraph Modeling for Unsupervised Person Re-identification](https://mlsanity.com/paper/2110.05886/) (2021)
- [Domain-Class Correlation Decomposition for Generalizable Person Re-Identification](https://mlsanity.com/paper/2106.15206/) (2021)
- [Unsupervised Person Re-Identification with Wireless Positioning under Weak Scene Labeling](https://mlsanity.com/paper/2110.15610/) (2021)
- [A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer](https://mlsanity.com/paper/2109.11719/) (2021)
- [Exploring the Diversity and Invariance in Yourself for Visual Pre-Training Task](https://mlsanity.com/paper/2106.00537/) (2021)
- [Cross-Camera Trajectories Help Person Retrieval in a Camera Network](https://mlsanity.com/paper/2204.12900/) (2022)

## Abstract (arXiv)

Existing methods for person re-identification (Re-ID) are mostly based on supervised learning which requires numerous manually labeled samples across all camera views for training. Such a paradigm suffers the scalability issue since in real-world Re-ID application, it is difficult to exhaustively label abundant identities over multiple disjoint camera views. To this end, we propose a progressive deep learning method for unsupervised person Re-ID in the wild by Tracklet Association with Spatio-Temporal Regularization (TASTR). In our approach, we first collect tracklet data within each camera by automatic person detection and tracking. Then, an initial Re-ID model is trained based on within-camera triplet construction for person representation learning. After that, based on the person visual feature and spatio-temporal constraint, we associate cross-camera tracklets to generate cross-camera triplets and update the Re-ID model. Lastly, with the refined Re-ID model, better visual feature of person can be extracted, which further promote the association of cross-camera tracklets. The last two steps are iterated multiple times to progressively upgrade the Re-ID model.