Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification (arxiv)

Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identification (PDM)

Pytorch Code for PDM

This code is based on mangye16, ZYK100 [1, 5].

1. Prepare the datasets.

• (1) RegDB Dataset [3]: The RegDB dataset can be downloaded from this website by submitting a copyright form.

  • (Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website).

• (2) SYSU-MM01 Dataset [4]: The SYSU-MM01 dataset can be downloaded from this website.

  • run python pre_process_sysu.py to pepare the dataset, the training data will be stored in ".npy" format.

• (3) LLCM Dataset [5]: The LLCM dataset can be downloaded by sending a signed dataset release agreement copy to zhangyk@stu.xmu.edu.cn.

2. Training.

Train a model by:

python train_pdm_os.py --dataset sysu --gpu 0 --protos 10

--dataset: which dataset "llcm", "sysu" or "regdb".

--gpu: which gpu to run.

You may need mannully define the data path first.

Parameters: More parameters can be found in the script.

3. Testing.

Test a model on LLCM, SYSU-MM01 or RegDB dataset by

python test_pdm.py --mode all --tvsearch True --resume 'model_path' --gpu 0 --dataset sysu --protos 10

--dataset: which dataset "llcm", "sysu" or "regdb".

--mode: "all" or "indoor" all search or indoor search (only for sysu dataset).

--tvsearch: whether thermal to visible search (only for RegDB dataset).

--resume: the saved model path.

--gpu: which gpu to run.

4. References

[1] M. Ye, J. Shen, G. Lin, T. Xiang, L. Shao, and S. C., Hoi. Deep learning for person re-identification: A survey and outlook. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020.

[2] M. Ye, X. Lan, Z. Wang, and P. C. Yuen. Bi-directional Center-Constrained Top-Ranking for Visible Thermal Person Re-Identification. IEEE Transactions on Information Forensics and Security (TIFS), 2019.

[3] D. T. Nguyen, H. G. Hong, K. W. Kim, and K. R. Park. Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors, 17(3):605, 2017.

[4] A. Wu, W.-s. Zheng, H.-X. Yu, S. Gong, and J. Lai. Rgb-infrared crossmodality person re-identification. In IEEE International Conference on Computer Vision (ICCV), pages 5380–5389, 2017.

[5] Zhang Y, Wang H. Diverse embedding expansion network and low-light cross-modality benchmark for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2023: 2153-2162.