oclreid
[video input] Person Re-Identification for Robot Person Following with Online Continual Learning
Science Score: 26.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
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○DOI references
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○Scientific vocabulary similarity
Low similarity (9.8%) to scientific vocabulary
Repository
[video input] Person Re-Identification for Robot Person Following with Online Continual Learning
Basic Info
- Host: GitHub
- Owner: MedlarTea
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 217 MB
Statistics
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
OCLReID
This project is for target person tracking based on mmtrack framework. For running this code with robot/rosbag, please refer to OCL-RPF
Install
For Video Running Only
Create a conda environment and install OCLReID (based on mmtrack), worked in RTX3090 ```bash git clone https://github.com/MedlarTea/OCLReID cd OCLReID conda create -n oclreid python=3.7 conda activate oclreid conda install pytorch=1.11 cudatoolkit=11.3 torchvision=0.12.0 -c pytorch pip install mmcv-full==1.5.3 -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html pip install mmdet==2.26.0 pip install -r requirements.txt pip install -r requirements/build.txt pip install -v -e .
install orientation estimation method
cd mmtrack/models/orientation pip install -r requirements.txt pip install -v -e . ```
Download pre-trained weights for OCLReID
- Download 2d joint detection models: Google drive and put the checkpoints to OCLReID/mmtrack/models/pose/Models/sppe.
- Download ReID models: Google drive, then make directory OCLReID/checkpoints/reid and put the checkpoints to it.
Run It!
Video Running
bash
cd OCLReID
python run_video.py --show_result
This would run the ./demo.mp4.
Run on the customized dataset
Our customized dataset is provided in dataset directory with four scenarios: corridor1, corridor2, lab_corridor and room. We provide raw_video.mp4 and labels.txt for each scenario. Specifically, bbox annotations in the label.txt are represented as x1,y1,w,h.
Note: the annotations are rough, but should be enough for evaluating the ReID performance of algorithms.
Citation
@article{ye2024oclrpf,
title={Person re-identification for robot person following with online continual learning},
author={Ye, Hanjing and Zhao, Jieting and Zhan, Yu and Chen, Weinan and He, Li and Zhang, Hong},
journal={IEEE Robotics and Automation Letters},
year={2024},
publisher={IEEE}
}
Owner
- Name: MedlarTea
- Login: MedlarTea
- Kind: user
- Repositories: 3
- Profile: https://github.com/MedlarTea
GitHub Events
Total
- Watch event: 6
- Push event: 8
- Fork event: 2
- Create event: 2
Last Year
- Watch event: 6
- Push event: 8
- Fork event: 2
- Create event: 2
Dependencies
- pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
- Cython *
- EasyDict *
- json_tricks *
- opencv-python *
- pandas *
- pyyaml *
- scikit-image *
- scipy *
- shapely *
- tensorboardX *
- yacs >=0.1.5
- matplotlib >=3.0
- numpy >=1.15
- Pillow *
- loguru *
- ninja *
- numpy *
- onnx ==1.8.1
- onnx-simplifier ==0.3.5
- onnxruntime ==1.8.0
- opencv_python *
- scikit-image *
- tabulate *
- tensorboard *
- thop *
- torch >=1.7
- torchvision *
- tqdm *
- cython *
- numba ==0.53.0
- numpy *
- fitlog *
- imageio *
- visdom *
- myst_parser *
- sphinx ==4.0.2
- sphinx-copybutton *
- sphinx_markdown_tables *
- mmcls >=0.16.0
- mmcv-full >=1.3.17,<1.6.0
- mmdet >=2.19.1,<3.0.0
- mmcls *
- mmcv *
- mmdet *
- torch *
- torchvision *
- attributee ==0.1.5
- dotty_dict *
- lap *
- matplotlib *
- mmcls >=0.16.0
- motmetrics *
- packaging *
- pandas <=1.3.5
- pycocotools <=2.0.2
- scipy <=1.7.3
- seaborn *
- terminaltables *
- tqdm *
- asynctest * test
- codecov * test
- flake8 * test
- interrogate * test
- isort ==4.3.21 test
- kwarray * test
- pytest * test
- ubelt * test
- xdoctest >=0.10.0 test
- yapf * test
- Pillow >=7.1.2
- PyYAML >=5.3.1
- catkin_pkg *
- defusedxml *
- easydict *
- fitlog *
- imageio *
- loguru ==0.5.3
- matplotlib >=3.2.2
- numpy >=1.18.5
- opencv-python >=4.1.2
- pandas >=1.1.4
- pycocotools ==2.0.0
- requests >=2.23.0
- rosnumpy *
- rospkg *
- scikit-learn *
- scipy >=1.4.1
- seaborn >=0.11.0
- sympy *
- tabulate ==0.8.9
- thop ==0.0.31.post2005241907
- torch >=1.7.0
- torchmetrics *
- torchvision >=0.8.1
- tqdm >=4.41.0
- visdom *