TencentYoutuResearch / PersonReID-TSF

Code for AAAI 2020 paper Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect.

Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect (AAAI 2020)

Code for AAAI 2020 paper Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect. If you find this code useful in your research, please consider citing:

@article{jiang2020rethinking,
  title={Rethinking Temporal Fusion for Video-based Person Re-identification on Semantic and Time Aspect},
  author={Xinyang Jiang, Yifei Gong, Xiaowei Guo, Qize Yang, Feiyue Huang, Weishi Zheng, Feng Zheng, Xing Sun},
  booktitle={AAAI},
  year={2020}
}

Requirements

• python 2.7
• pytorch 0.3.1
• MARS, iLIDS or other datasets.

Training

Prepare Datasets

1. File Directory: ├── partitions.pkl ├── images │ ├── 0000000_0000_000000.png │ ├── 0000001_0000_000001.png │ ├── ...

2. Rename the images in following convention: "000000_000_000000.png" where the first substring splitted by underline is the person identity; for the second substring, the first digit is the camera id and the rest is track id; and the third substring is an image offset.

3. "partitions.pkl" file This file contains a python dictionary storing meta data of the datasets, which contains folling key value pairs "train_im_names": [list of image names] #storing a list of names of training images "train_ids2labels":{"identity":label} #a map that maps the person identity string to a integer label "val_im_names": [list of image names] #storing a list of names of validation images "test_im_names": [list of image names] #storing a list of names of testing images "test_marks"/"val_marks": [list of 0/1] #0/1 indicates if an image is in gallery

4. Other dataset directory structure To use other directory structure, override the data loader in "stf/Dataset/dataset.py"

Run Script

Start training by running script train.sh

Testing

Start testing by runing script train.sh

Model Framework

Model Performance