A codebase for video-based person re-identification

Salient-to-Broad Transition for Video Person Re-identification (CVPR 2022)

SANet: Statistic Attention Network for Video-Based Person Re-Identification (TCSVT 2021)

Get started

  # Train
  python main.py \
   --arch ${sinet, sbnet, idnet, sanet} \
   --dataset ${mars, lsvid, ...} \
   --root ${path of dataset} \
   --gpu_devices 0,1 \
   --save_dir ${path for saving modles and logs} \
  
  # Test with all frames
  python main.py \
   --arch ${sinet, sbnet, idnet, sanet} \
   --dataset mars \
   --root ${path of dataset} \
   --gpu_devices 0,1 \
   --save_dir ${path for saving logs} \
   --evaluate --all_frames --resume ${path of pretrained model}

Pretrained models

MARS

Methods	Paper	Reproduce	Download
SBNet (ResNet50 + SBM)	85.7/90.2	85.6/90.7	model
IDNet (Resnet50 + IDM)	85.9/90.5	85.9/90.4	model
SINet (ResNet50 + SBM + IDM)	86.2/91.0	86.3/90.9	model
SANet (ResNet50 + SA Block)	86.0/91.2	86.7/91.2	model

LS-VID

Methods	Paper	Reproduce	Download
SBNet (ResNet50 + SBM)	77.1/85.1	77.2/85.3	model
IDNet (Resnet50 + IDM)	78.0/86.2	78.2/86.0	model
SINet (ResNet50 + SBM + IDM)	79.6/87.4	79.9/87.2	model

Citation

If you use our code in your research or wish to refer to the baseline results, please use the following BibTeX entry.

@inproceedings{bai2022SINet,
    title={Salient-to-Broad Transition for Video Person Re-identification},
    author={Bai, Shutao and Ma, Bingpeng and Chang, Hong and Huang, Rui and Chen, Xilin},
    booktitle={CVPR},
    year={2022},
}

@ARTICLE{9570321,
  author={Bai, Shutao and Ma, Bingpeng and Chang, Hong and Huang, Rui and Shan, Shiguang and Chen, Xilin},
  journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
  title={SANet: Statistic Attention Network for Video-Based Person Re-Identification}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TCSVT.2021.3119983}
}

Acknowledgments

This code is based on the implementations of AP3D.