# Faster Person Re-Identification

arXiv: https://arxiv.org/abs/2008.06826 · HTML: https://mlsanity.com/paper/2008.06826/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2020.

## Built on by (most cited first, 15 of 15)

- [Large-Scale Spatio-Temporal Person Re-identification: Algorithms and Benchmark](https://mlsanity.com/paper/2105.15076/) (2021)
- [Combined Depth Space based Architecture Search For Person Re-identification](https://mlsanity.com/paper/2104.04163/) (2021)
- [Deep learning-based person re-identification methods: A survey and outlook of recent works](https://mlsanity.com/paper/2110.04764/) (2021)
- [Learning to Disentangle Scenes for Person Re-identification](https://mlsanity.com/paper/2111.05476/) (2021)
- [Ground-to-Aerial Person Search: Benchmark Dataset and Approach](https://mlsanity.com/paper/2308.12712/) (2023)
- [Unsupervised Person Re-Identification with Multi-Label Learning Guided Self-Paced Clustering](https://mlsanity.com/paper/2103.04580/) (2021)
- [Masked Attribute Description Embedding for Cloth-Changing Person Re-identification](https://mlsanity.com/paper/2401.05646/) (2024)
- [HashReID: Dynamic Network with Binary Codes for Efficient Person Re-identification](https://mlsanity.com/paper/2308.11900/) (2023)
- [The detection and rectification for identity-switch based on unfalsified control](https://mlsanity.com/paper/2307.14591/) (2023)
- [AR2-4FV: Anchored Referring and Re-identification for Long-Term Grounding in Fixed-View Videos](https://mlsanity.com/paper/2603.07758/) (2026)
- [Multi-granularity for knowledge distillation](https://mlsanity.com/paper/2108.06681/) (2021)
- [Rapid Person Re-Identification via Sub-space Consistency Regularization](https://mlsanity.com/paper/2207.05933/) (2022)
- [AaP-ReID: Improved Attention-Aware Person Re-identification](https://mlsanity.com/paper/2309.15780/) (2023)
- [Evolution of ReID: From Early Methods to LLM Integration](https://mlsanity.com/paper/2506.13039/) (2025)
- [A Generative AI Integrated Multimodal Framework for Low-Latency Multi-Camera Person Re-Identification](https://mlsanity.com/paper/2609.14419/) (2026)

## Abstract (arXiv)

Fast person re-identification (ReID) aims to search person images quickly and accurately. The main idea of recent fast ReID methods is the hashing algorithm, which learns compact binary codes and performs fast Hamming distance and counting sort. However, a very long code is needed for high accuracy (e.g. 2048), which compromises search speed. In this work, we introduce a new solution for fast ReID by formulating a novel Coarse-to-Fine (CtF) hashing code search strategy, which complementarily uses short and long codes, achieving both faster speed and better accuracy. It uses shorter codes to coarsely rank broad matching similarities and longer codes to refine only a few top candidates for more accurate instance ReID. Specifically, we design an All-in-One (AiO) framework together with a Distance Threshold Optimization (DTO) algorithm. In AiO, we simultaneously learn and enhance multiple codes of different lengths in a single model. It learns multiple codes in a pyramid structure, and encourage shorter codes to mimic longer codes by self-distillation. DTO solves a complex threshold search problem by a simple optimization process, and the balance between accuracy and speed is easily controlled by a single parameter. It formulates the optimization target as a Fβ score that can be optimised by Gaussian cumulative distribution functions. Experimental results on 2 datasets show that our proposed method (CtF) is not only 8% more accurate but also 5x faster than contemporary hashing ReID methods. Compared with non-hashing ReID methods, CtF is 50× faster with comparable accuracy. Code is available at https://github.com/wangguanan/light-reid.