Authors: Ruiyang Ha,Songyi Jiang,Bin Li,Bikang Pan,Yihang Zhu,Junjie Zhang,Xiatian Zhu,Shaogang Gong,Jingya Wang
Paper: https://arxiv.org/abs/2503.17096
Conventional person re-identification (ReID) research is often limited to single-modality sensor data from static cameras, which fails to address the complexities of real-world scenarios where multi-modal signals are increasingly prevalent. For instance, consider an urban ReID system integrating stationary RGB cameras, nighttime infrared sensors, and UAVs equipped with dynamic tracking capabilities. Such systems face significant challenges due to variations in camera perspectives, lighting conditions, and sensor modalities, hindering effective person ReID. To address these challenges, we introduce the MP-ReID benchmark, a novel dataset designed specifically for multi-modality and multi-platform ReID. This benchmark uniquely compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging, captured by both UAVs and ground-based cameras in indoor and outdoor environments. Building on this benchmark, we introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios. Our method consistently outperforms state-of-the-art approaches, establishing a robust foundation for future research in complex and dynamic ReID environments.
You can see more details in dataset page. Please fill in the agreement and send it to [email protected] to get the MP-ReID Dataset.
conda create -n UniPrompt python=3.8
conda activate UniPrompt
conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia
pip install -r requirements.txt
For example, if you want to train the baseline in cctv_ir_cctv_rgb set, you need to modify the bottom of configs/ours/cctv_ir_cctv_rgb.yml to
DATASETS:
NAMES: ('mmmp')
ROOT_DIR: ('your_dataset_dir')
EXP_SETTING: ('exp_cctv_ir_cctv_rgb')
OUTPUT_DIR: 'your_output_dir'
then run
CUDA_VISIBLE_DEVICES=0 python train.py --config_file configs/ours/cctv_ir_cctv_rgb.yml
We are also providing additional files that might be used during the training phase. Please refer to the link in the Dataset section for more details.
Cite as below if you find this repository is helpful to your project:
@article{ha2025multi,
title={Multi-modal Multi-platform Person Re-Identification: Benchmark and Method},
author={Ha, Ruiyang and Jiang, Songyi and Li, Bin and Pan, Bikang and Zhu, Yihang and Zhang, Junjie and Zhu, Xiatian and Gong, Shaogang and Wang, Jingya},
journal={arXiv preprint arXiv:2503.17096},
year={2025}
}