Video-based Cloth-Changing Person Re-Identification

This repository contains implementation of training and testing baseline for Video-based Cloth-Changing Person Re-ID (VCCRe-ID). This is part of the official implementation for the paper: Temporal 3D Shape Modeling for Video-based Cloth-changing Person Re-Identification

News

E-VCCR dataset will be released soon.

1. Features

Supported CNN backbones

c2dres50: C2DResNet50
i3dres50: I3DResNet50
ap3dres50: AP3DResNet50
nlres50: NLResNet50
ap3dnlres50: AP3DNLResNet50

Summary of VCCRe-ID datasets

This baseline currently supports the public VCCRe-ID datasets: VCCR, CCVID, and CCPG.

Dataset	Num.IDs	Num.Tracklets	Num.Clothes/ID	Public	Download
Motion-ReID	30	240	-	X	-
CVID-reID	90	2980	-	X	-
SCCVRe-ID	333	9620	2~37	X	-
RCCVRe-ID	34	6948	2~10	X	-
CCPG	200	~16k	-	Per Request	project link
CCVID	226	2856	2~5	Yes	link
VCCR	392	4384	2~10	Yes	link

2. Running instructions

Getting started

Create virtual environment

First, create a virtual environment for the repository

conda create -n vccreid python=3.8

then activate the environment

conda activate vccreid

Clone the repository

git clone https://github.com/dustin-nguyen-qil/VCCReID-Baseline.git

Next, install the dependencies by running ...

pip install -r requirements.txt

Data Preparation

Download the datasets VCCR and CCVID following download links above
Create a folder named data inside the repository
Run the following command line (Note: replace the path to the folder storing the datasets and the dataset name)

python datasets/prepare.py --root "/media/dustin/DATA/Research/Video-based ReID" --dataset_name vccr

Configuration options

Go to ./config.py to modify configurations accordingly

Dataset name
Number of epochs
Batch size
Learning rate
CNN backbone (according to model names above)
Choice of loss functions

If training from checkpoint, copy checkpoint path and paste to RESUME in ./config.py.

Run baseline

Create a folder named work_space inside the repository, then create two subfolders named save and output.

data
work_space
|--- save
|--- output
main.sh

Run

bash main.sh

Trained model will be automatically saved to work_space/save. Testing results will be automatically saved to work_space/output.

Citation

If you find this repo helpful, please cite:

@InProceedings{Nguyen_2024_WACV,
    author    = {Nguyen, Vuong D. and Mantini, Pranav and Shah, Shishir K.},
    title     = {Temporal 3D Shape Modeling for Video-Based Cloth-Changing Person Re-Identification},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops},
    month     = {January},
    year      = {2024},
    pages     = {173-182}
}

Acknowledgement

Related repos:

Simple-CCReID.