Spatial-Temporal Person Re-identification

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Code for st-ReID(pytorch). We achieve Rank@1=98.1%, mAP=87.6% without re-ranking and Rank@1=98.0%, mAP=95.5% with re-ranking for market1501.For Duke-MTMC, we achieve Rank@1=94.4%, mAP=83.9% without re-ranking and Rank@1=94.5%, mAP=92.7% with re-ranking.

1. ST-ReID

1.1 model

1.2 result

2. rerequisites

• Pytorch 0.3
• Python 3.6
• Numpy

3. experiment

Market1501

1. data prepare
   

   1. change the path of dataset
      
   2. python3 prepare --Market

2. train (appearance feature learning)
   python3 train_market.py --PCB --gpu_ids 2 --name ft_ResNet50_pcb_market_e --erasing_p 0.5 --train_all --data_dir "/home/huangpg/st-reid/dataset/market_rename/"

3. test (appearance feature extraction)
   python3 test_st_market.py --PCB --gpu_ids 2 --name ft_ResNet50_pcb_market_e --test_dir "/home/huangpg/st-reid/dataset/market_rename/"

4. generate st model (spatial-temporal distribution)
   python3 gen_st_model_market.py --name ft_ResNet50_pcb_market_e --data_dir "/home/huangpg/st-reid/dataset/market_rename/"

5. evaluate (joint metric, you can use your own visual feature or spatial-temporal streams)
   python3 evaluate_st.py --name ft_ResNet50_pcb_market_e

6. re-rank
   6.1) python3 gen_rerank_all_scores_mat.py --name ft_ResNet50_pcb_market_e
   6.2) python3 evaluate_rerank_market.py --name ft_ResNet50_pcb_market_e

DukeMTMC-reID

1. data prepare
   python3 prepare --Duke

2. train (appearance feature learning)
   python3 train_duke.py --PCB --gpu_ids 2 --name ft_ResNet50_pcb_duke_e --erasing_p 0.5 --train_all --data_dir "/home/huangpg/st-reid/dataset/DukeMTMC_prepare/"

3. test (appearance feature extraction)
   python3 test_st_duke.py --PCB --gpu_ids 2 --name ft_ResNet50_pcb_duke_e --test_dir "/home/huangpg/st-reid/dataset/DukeMTMC_prepare/"

4. generate st model (spatial-temporal distribution)
   python3 gen_st_model_duke.py --name ft_ResNet50_pcb_duke_e --data_dir "/home/huangpg/st-reid/dataset/DukeMTMC_prepare/"

5. evaluate (joint metric, you can use your own visual feature or spatial-temporal streams)
   python3 evaluate_st.py --name ft_ResNet50_pcb_duke_e

6. re-rank
   6.1) python3 gen_rerank_all_scores_mat.py --name ft_ResNet50_pcb_duke_e
   6.2) python3 evaluate_rerank_duke.py --name ft_ResNet50_pcb_duke_e

Citation

If you use this code, please kindly cite it in your paper.

@article{guangcong2019aaai,
  title={Spatial-Temporal Person Re-identification},
  author={Wang, Guangcong and Lai, Jianhuang and Huang, Peigen and Xie, Xiaohua},
  booktitle={AAAI},
  year={2019}
}

Related Repos

Our codes are mainly based on this repository