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https://github.com/dawnyc/ROMTrack
[ICCV 2023] Robust Object Modeling for Visual Tracking, Official Implementation
https://github.com/dawnyc/ROMTrack
iccv2023 object-modeling pytorch robustness tracking transformer
Last synced: about 2 months ago
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[ICCV 2023] Robust Object Modeling for Visual Tracking, Official Implementation
- Host: GitHub
- URL: https://github.com/dawnyc/ROMTrack
- Owner: dawnyc
- License: mit
- Created: 2023-08-06T12:53:42.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2024-05-04T02:18:01.000Z (5 months ago)
- Last Synced: 2024-06-17T00:33:58.175Z (3 months ago)
- Topics: iccv2023, object-modeling, pytorch, robustness, tracking, transformer
- Language: Python
- Homepage: https://arxiv.org/abs/2308.05140
- Size: 5.96 MB
- Stars: 38
- Watchers: 4
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# ROMTrack
The official implementation of the ICCV 2023 paper [*Robust Object Modeling for Visual Tracking*](https://arxiv.org/abs/2308.05140)
[[CVF Open Access]](https://openaccess.thecvf.com/content/ICCV2023/papers/Cai_Robust_Object_Modeling_for_Visual_Tracking_ICCV_2023_paper.pdf
) [[Poster]](asset/Poster.pdf) [[Video]](https://www.bilibili.com/video/BV1p84y1d7ja/)
[[Models and Raw Results]](https://drive.google.com/drive/folders/1Q7CpNIhWX05VU7gECnhePu3dKzTV_VoK?usp=drive_link) (Google Drive) [[Models and Raw Results]](https://pan.baidu.com/s/1JsOh_YKPmVAdJwn_XcUg5g) (Baidu Netdisk: romt)
#### Base Models
| Variant | ROMTrack | ROMTrack-384 |
| :----------------------------------: | :--------------------------: | :--------------------------: |
| Model Setting | ViT-Base | ViT-Base |
| Pretrained Method | MAE | MAE |
| Pretrained Weight |[MAE checkpoint](https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth)|[MAE checkpoint](https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth)|
| Template / Search | 128×128 / 256×256 | 192×192 / 384×384 |
| GOT-10k
(AO / SR 0.5 / SR 0.75) | 72.9 / 82.9 / 70.2 | 74.2 / 84.3 / 72.4 |
| LaSOT
(AUC / Norm P / P) | 69.3 / 78.8 / 75.6 | 71.4 / 81.4 / 78.2 |
| TrackingNet
(AUC / Norm P / P) | 83.6 / 88.4 / 82.7 | 84.1 / 89.0 / 83.7 |
| LaSOT_ext
(AUC / Norm P / P) | 48.9 / 59.3 / 55.0 | 51.3 / 62.4 / 58.6 |
| TNL2K
(AUC / Norm P / P) | 56.9 / 73.7 / 58.1 | 58.0 / 75.0 / 59.6 |
| NFS / OTB / UAV
(AUC) | 68.0 / 71.4 / 69.7 | 68.8 / 70.9 / 70.5 |
| VOT2020 BBox
(EAO / A / R) | 0.326 / 0.480 / 0.816 | 0.329 / 0.483 / 0.822 |
| GPU FPS / MACs(G) / Params(M) | 116 / 34.5 / 92.1 | 67 / 77.7 / 92.1 |
| CPU FPS | 9.9 | 3.0 |
#### Extended Models (Efficiency-Oriented)
| Variant | ROMTrack-Tiny-256 | ROMTrack-Small-256 |
| :----------------------------------: | :--------------------------: | :--------------------------: |
| Model Setting | ViT-Tiny | ViT-Small |
| Pretrained Method | Supervised on ImageNet-22k | Supervised on ImageNet-22k |
| Pretrained Weight |[Timm checkpoint](https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz)|[Timm checkpoint](https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz)|
| Template / Search | 128×128 / 256×256 | 128×128 / 256×256 |
| LaSOT
(AUC / Norm P / P) | 59.3 / 68.8 / 60.4 | 62.3 / 72.3 / 65.3 |
| TrackingNet
(AUC / Norm P / P) | 75.8 / 81.7 / 71.5 | 78.5 / 84.3 / 75.3 |
| LaSOT_ext
(AUC / Norm P / P) | 40.4 / 49.7 / 43.1 | 43.2 / 52.9 / 47.1 |
| TNL2K
(AUC / Norm P / P) | 48.6 / 64.4 / 45.5 | 52.0 / 68.7 / 50.5 |
| NFS / OTB / UAV
(AUC) | 62.5 / 68.5 / 62.9 | 65.3 / 68.9 / 66.4 |
| VOT2020 BBox
(EAO / A / R) | 0.265 / 0.459 / 0.704 | 0.297 / 0.477 / 0.764 |
| GPU FPS / MACs(G) / Params(M) | 466 / 2.7 / 8.0 | 236 / 9.3 / 25.4 |
| CPU FPS | 36.6 | 17.2 |
#### Extended Models (Performance-Oriented)
| Variant | ROMTrack-Large-384 |
| :----------------------------------: | :--------------------------: |
| Model Setting | ViT-Large |
| Pretrained Method | MAE |
| Pretrained Weight |[MAE checkpoint](https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_large.pth)|
| Template / Search | 192×192 / 384×384 |
| LaSOT
(AUC / Norm P / P) | 72.0 / 81.7 / 79.1 |
| TrackingNet
(AUC / Norm P / P) | 85.2 / 89.8 / 85.4 |
| LaSOT_ext
(AUC / Norm P / P) | 52.9 / 64.3 / 60.9 |
| TNL2K
(AUC / Norm P / P) | 60.4 / 77.7 / 63.9 |
| NFS / OTB / UAV
(AUC) | 69.2 / 71.0 / 71.5 |
| VOT2020 BBox
(EAO / A / R) | 0.338 / 0.492 / 0.820 |
| GPU FPS / MACs(G) / Params(M) | 21 / 266.5 / 311.3 |
| CPU FPS | 1.1 |
## :newspaper: News
**[May 2, 2024]**
- We release the extended models ***ROMTrack-Large-384*** for Performance-Oriented Visual Tracking!
- Models and Raw Results for all versions of ROMTrack are available on Google Drive or Baidu Netdisk.
- Code and script for VOT2020 evaluation are available now.
**[April 18, 2024]**
- We release the extended models ***ROMTrack-Tiny-256*** and ***ROMTrack-Small-256*** for Efficient Visual Tracking!
- We provide detailed information for all versions of ROMTrack, see **Base Models** and **Extended Models** above.
**[April 17, 2024]**
- Repository Upgrade is already done! Training and Evaluation using PyTorch 2.2.0 and Python 3.8 brings more efficiency.
- Training and Evaluation Devices for the upgraded code: RTX A6000, Intel(R) Xeon(R) Silver 4314 CPU @ 2.40GHz, Ubuntu 20.04.1 LTS.
**[March 25, 2024]**
- We upgrade the implementation to Python 3.8 and PyTorch 2.2.0!
- We update results on TNL2K!
- We update FPS metrics on RTX A6000 GPU for reference.
**[March 21, 2024]**
- We update 2 radar plots for visualization on LaSOT and LaSOT_ext.
- We post a blog on [Zhihu](https://zhuanlan.zhihu.com/p/662351482), welcome for reading.
**[October 18, 2023]**
- We update paper in CVF Open Access version.
- We release poster and video.
**[September 21, 2023]**
- We release Models and Raw Results of ROMTrack.
- We refine README for more details.
**[August 6, 2023]**
- We release Code of ROMTrack.
**[July 14, 2023]**
- ROMTrack is accepted to **ICCV2023**!
## :calendar: TODO
- [x] Extended Models (Efficiency-Oriented & Performance-Oriented) for ROMTrack
- [x] Repository Upgrade
- [x] More Analysis (Radar Plot) and More Results (TNL2K Dataset)
- [x] Code for ROMTrack
- [x] Model Zoo and Raw Results
- [x] Refine README
## :star: Highlights
### :rocket: New Tracking Framework pursing Robustness
- ROMTrack employes a robust object modeling design which can keep the inherent information of the target template and enables mutual feature matching between the target and the search region simultaneously.
- **Robustness Comparison** with SOTA methods (bounding box only) on VOT2020.
### :rocket: Strong Performance and Comparable Speed
- Performance on Benchmarks
- Radar Analysis on LaSOT and LaSOT_ext
- Speed, MACs, Params (Test on 1080Ti)
## :book: Install the environment
Use the Anaconda
```
conda create -n romtrack python=3.8
conda activate romtrack
bash install_pytorch.sh
```
## :book: Data Preparation
Put the tracking datasets in ./data. It should look like:
```
${ROMTrack_ROOT}
-- data
-- lasot
|-- airplane
|-- basketball
|-- bear
...
-- lasot_ext
|-- atv
|-- badminton
|-- cosplay
...
-- got10k
|-- test
|-- train
|-- val
-- coco
|-- annotations
|-- train2017
-- trackingnet
|-- TRAIN_0
|-- TRAIN_1
...
|-- TRAIN_11
|-- TEST
```
## :book: Set project paths
Run the following command to set paths for this project
```
python tracking/create_default_local_file.py --workspace_dir . --data_dir ./data --save_dir .
```
After running this command, you can also modify paths by editing these two files
```
lib/train/admin/local.py # paths about training
lib/test/evaluation/local.py # paths about testing
```
## :book: Train ROMTrack
Training with multiple GPUs using DDP. More details of other training settings can be found at ```tracking/train_romtrack.sh```
```
bash tracking/train_romtrack.sh
```
## :book: Test and evaluate ROMTrack on benchmarks
- LaSOT/LaSOT_ext/GOT10k-test/TrackingNet/OTB100/UAV123/NFS30.
- More details of test settings can be found at ```tracking/test_romtrack.sh```
```
bash tracking/test_romtrack.sh
```
- VOT2020. Current version is vot-toolkit(==0.5.3) and vot-trax(==3.0.3).
- Take ROMTrack-Large-384 below as an example.
```
### Evaluate ROMTrack-Large-384 with AlphaRefine
vot evaluate --workspace ./external/vot2020/ROMTrack_large_384 ROMTrack_large_384_AR
vot analysis --nocache --workspace ./external/vot2020/ROMTrack_large_384 ROMTrack_large_384_AR
### Evaluate ROMTrack-Large-384 without AlphaRefine
vot evaluate --workspace ./external/vot2020/ROMTrack_large_384 ROMTrack_large_384
vot analysis --nocache --workspace ./external/vot2020/ROMTrack_large_384 ROMTrack_large_384
```
## :book: Compute FLOPs/Params and test speed
```
bash tracking/profile_romtrack.sh
```
## :book: Visualization
We provide attention maps and feature maps for several sequences on LaSOT. Detailed analysis can be found in our paper.
## :bookmark: Acknowledgments
* Thanks for [STARK](https://github.com/researchmm/Stark), [PyTracking](https://github.com/visionml/pytracking) and [MixFormer](https://github.com/MCG-NJU/MixFormer) Library, which helps us to quickly implement our ideas and test our performances.
* Our implementation of the ViT is modified from the [Timm](https://github.com/rwightman/pytorch-image-models) repo.
## :pencil: Citation
If our work is useful for your research, please feel free to star :star: and cite our paper:
```
@InProceedings{Cai_2023_ICCV,
author = {Cai, Yidong and Liu, Jie and Tang, Jie and Wu, Gangshan},
title = {Robust Object Modeling for Visual Tracking},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023},
pages = {9589-9600}
}
```