| # VDT-AGPReID |
| View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network (CVPR'24) [[paper_link]](https://arxiv.org/abs/2403.14513) |
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| ## [1] Dataset: CARGO |
|  |
| ### Introduction |
| * CARGO is a large-scale aerial-ground person re-identification (AGPReID) dataset, which captured from a synthesized scene in Unity3D. |
| * CARGO contains 13 cameras (8 ground and 5 aerial cameras), 5000 person IDs, and 108563 person images. |
| * **Camera 1 $\sim$ 5 belong to aerial cameras, and Camera 6 $\sim$ 13 belong to ground cameras.** |
| * In the aerial camera area, two different drone roaming strategies are designed according to the size of the surveillance area. For the small area (left area), we deploy one drone with a $90^\circ$ overhead view, allowing it to move counterclockwise around each street. For a large area (right area), we deploy individual drones on each of the four streets with a $45^\circ\sim60^\circ$ tilt view, allowing them to move back and forth on corresponding streets. |
| * Dataset Link: [Google Drive](https://drive.google.com/file/d/1yDjyH0VtW7efxP3vgQjIqTx2oafCB67t/view?usp=drive_link) |
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| ### Setting |
| * We split CARGO into the train (51,451 images with 2500 IDs) and test sets (51,024 images with the remaining 2500 IDs) with an almost 1:1 ratio. |
| * **Testing Protocol 1 (ALL)** uses full test data and labels, which focuses on the comprehensive retrieval performance. |
| * **Testing Protocol 2 (G $\leftrightarrow$ G)** only retains the data under the ground camera in the test set (60 query IDs with 134 images, 2404 gallery IDs with 18,444 images). |
| * **Testing Protocol 3 (A $\leftrightarrow$ A)** only retains the data under the aerial camera in the test set (89 query IDs with 178 images, 2447 gallery IDs with 32,268 images). |
| * **Testing Protocol 4 (A $\leftrightarrow$ G)** relabels the original test set into two domains (aerial and ground domain) based on the camera label. |
| * The training set of all testing protocols retains same. |
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|
| ### Annotation |
| Annotations are preserved in the name of each image by the format ``camID_time_personID_index.jpg''. |
| |
| > For example, ``Cam2_day_2519_320.jpg'' means that: |
| > * Camera id is 2, and it belongs to the aerial view. |
| > * Capture time is day. (day or night) |
| > * Person id is 2519. |
| > * Index is 320. (It has no practical meaning for you.) |
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| ### License |
| * The datasets can only be used for ACADEMIC PURPOSES. NO COMERCIAL USE is allowed. |
| * Copyright © Sun Yat-sen University. All rights reserved. |
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| ## [2] Method: View-decoupled Transformer |
|  |
| ### Requirements |
| #### Step1: Prepare enviorments |
| Please refer to [INSTALL.md](./INSTALL.md). |
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| #### Step2: Prepare datasets |
| Download the CARGO dataset and modify the dataset path. |
| Line 22, 60, 100 and 140 in [cargo.py](./fastreid/data/datasets/cargo.py) . |
| > self.data_dir = XXX |
| |
| #### Step3: Prepare ViT Pre-trained Models |
| Download the ViT-base Pre-trained model and modify the path. Line 11 in [VDT.yml](./configs/CARGO/VDT.yml): |
| > PRETRAIN_PATH: XXX |
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|
| ### Training & Testing |
| Training VDT on the CARGO dataset with one GPU: |
| ``` |
| CUDA_VISIBLE_DEVICES=0 python3 tools/train_net.py --config-file ./configs/CARGO/VDT.yml MODEL.DEVICE "cuda:0" |
| ``` |
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| Testing VDT on the CARGO dataset: |
| ``` |
| CUDA_VISIBLE_DEVICES=1 python3 tools/train_net.py --config-file ./configs/CARGO/VDT.yml --eval-only MODEL.WEIGHTS your_model_pth_path MODEL.DEVICE "cuda:0" |
| ``` |
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|
| ### Acknowledgement |
| Codebase from [fast-reid](https://github.com/JDAI-CV/fast-reid). So please refer to that repository for more usage. |
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| ## [3] Citation |
| If you find this code useful for your research, please kindly cite the following papers: |
| ``` |
| @InProceedings{Zhang_2024_CVPR, |
| author = {Zhang, Quan and Wang, Lei and Patel, Vishal M. and Xie, Xiaohua and Lai, Jian-Huang}, |
| title = {View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network}, |
| booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year = {2024} |
| } |
| ``` |
| If you have any question, please feel free to contact me. E-mail: zhangq48@mail2.sysu.edu.cn |
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