RGB-IR Cross Modality Person Re-Identification

Pytorch implementation of Domain Invariant and Attribute based Representation Learning for RGB-IR Cross Modality Person Re-Identification. Refer to the following report for details about the model architecture and the results.

Dataset Preparation

Download the following datasets and extract them in the datasets folder.

1. RegDB Dataset [1]: 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 [2]: The SYSU-MM01 dataset can be downloaded from this website.

Training and Testing

1. To train the DIRL model on RegDB dataset,

cd src/regdb_/
python train.py --arch resnetmid_domain --cuda --lambda-domain 1 --lambda-attribute 0

Similarly, we can train for SYSU dataset by running 'train.py' under 'src/sysu_' directory

2. To train the ABRL model on SYSU dataset,

cd src/sysu_/
python train.py --arch resnetmid --cuda --lambda-domain 0 --lambda-attribute 10 --test-on-val

Similarly, we can train for SYSU dataset by running 'train.py' under 'src/sysu_' directory

• One can refer to the 'OptionsParser' function in utils file to explore the training options and parameters for regDB dataset. Similarly, for SYSU dataset refer to this utils file.
• After training is completed, the model will be tested on the test dataset automatically.

References.

[1] 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.

[2] 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.