https://github.com/HUSTDML/CTTrack
https://github.com/HUSTDML/CTTrack
Last synced: about 2 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/HUSTDML/CTTrack
- Owner: HUSTDML
- License: mit
- Created: 2022-08-19T08:20:50.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-07-23T06:54:26.000Z (10 months ago)
- Last Synced: 2024-08-02T06:12:13.388Z (10 months ago)
- Language: Python
- Size: 1.21 MB
- Stars: 38
- Watchers: 4
- Forks: 5
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Visual-Object-Tracking - [code
README
# **CTTrack**
The official implementation of the AAAI 2023 Oral paper ** [[Compact Transformer Tracker with Correlative Masked Modeling]](https://arxiv.org/abs/2301.10938) **
[[Models and Raw results]](https://drive.google.com/drive/folders/1xmlOzwIu_2Jexzzl69MCPFXBip1MgSbf?usp=sharing) (Google Driver) [[Models and Raw results]](https://pan.baidu.com/s/1NGX2BlkkfZlGSciTI7XNpg) (Baidu Driver: LRBW)
## News
**[ Jan 18, 2023]**
- We release Code, models and raw results.
**[ Jan 11, 2023]**
- Our paper is selected for an **oral** presentation.
**[ Nov 19, 2022]**
- CTTrack is accepted to **AAAI2023** .
## Strong performance
| Tracker | UAV123 (P) | LaSOT (NP) | TrackingNet (AUC) | GOT-10k (AO) |
| --------------------- | ---------- | ---------- | ----------------- | ------------ |
| **CTTrack** | **93.3** | 79.7 | **84.9** | **72.8** |
| MixFormer* (CVPR2022) | 91.0 | 79.9 | 83.9 | 70.7 |
| CSWinTT* (CVPR2022) | 90.3 | 75.2 | 81.9 | 69.4 |
| UTT* (CVPR2022) | - | - | 79.7 | 67.2 |
| STARK* (ICVV2021) | - | 77.0 | 82.0 | 68.8 |
| TransT* (CVPR2021) | 87.6 | 73.8 | 81.4 | 67.1 |
| TrDiMP* (CVPR2021) | 87.6 | 73.2 | 78.4 | 68.8 |
| STMTrack* (CVPR2021) | - | 69.3 | 80.3 | 64.2 |
| AutoMatch* (ICVV2021) | 83.8 | 67.5 | 76.0 | 65.2 |
| SiamGAT* (CVPR2021) | 84.3 | 63.3 | - | 62.7 |
| KYS* (ECCV2020) | - | 63.3 | 74.0 | 63.6 |
| SiamAttn* (CVPR2020) | 84.5 | 64.8 | 75.2 | - |
| SiamFC++* (AAAI2020) | 80.4 | 62.3 | 75.4 | 59.5 |
| SiamRPN++* (CVPR2019) | 84.0 | 56.9 | 73.3 | 51.7 |
| DiMP* (ICCV2019) | 84.9 | 66.4 | 74.0 | 61.1 |
| ATOM* (CVPR2019) | 82.7 | 57.6 | 70.3 | 55.6 |
## Install the environment
```
conda create -n cttrack python=3.7
conda activate cttrack
pip install -r requirements.txt
```
## Data Preparation
Put the tracking datasets in ./data. It should look like:
```
${CTTRACK_ROOT}
-- data
-- lasot
|-- airplane
|-- basketball
|-- bear
...
-- got10k
|-- test
|-- train
|-- val
-- trackingnet
|-- TRAIN_0
|-- TRAIN_1
...
|-- TRAIN_11
|-- TEST
```
## 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
```
## Train CTTrack
### Train CTTrack-B
```
python tracking/train.py --script cttrack --config baseline --save_dir . --mode single
python tracking/train.py --script cttrack_online --config baseline --save_dir . --mode single --script_prv cttrak --config_prv baseline
```
### Train CTTrack-L
```
python tracking/train.py --script cttrack --config baseline_L --save_dir . --mode single
python tracking/train.py --script cttrack_online --config baseline_L --save_dir . --mode single --script_prv cttrak --config_prv baseline
```
## Test and evaluate CTTrack on benchmarks
### Test CTTrack-B
- OTB2015
```
python tracking/test.py cttrack baseline --dataset otb --threads 32
```
- UAV123
```
python tracking/test.py cttrack baseline --dataset uav --threads 32
```
- LaSOT
```
python tracking/test.py cttrack baseline --dataset lasot --threads 32
```
- GOT10K-test
```
python tracking/test.py cttrack baseline --dataset got10k_test --threads 32
```
- TrackingNet
```
python tracking/test.py cttrack baseline --dataset trackingnet --threads 32
```
### Test CTTrack-L
- OTB2015
```
python tracking/test.py cttrack baseline_L --dataset otb --threads 32
```
- UAV123
```
python tracking/test.py cttrack baseline_L --dataset uav --threads 32
```
- LaSOT
```
python tracking/test.py cttrack baseline_L --dataset lasot --threads 32
```
- GOT10K-test
```
python tracking/test.py cttrack baseline_L --dataset got10k_test --threads 32
```
- TrackingNet
```
python tracking/test.py cttrack baseline_L --dataset trackingnet --threads 32
```
### Evaluate CTTrack
**LaSOT/GOT10k-test/TrackingNet/OTB100/UAV123**
```
python tracking/analysis_results.py {script} {config} {dataset_name}
```
**For example**
```
python tracking/analysis_results.py cttrack baseline trackingnet
```
**VOT2020**
Before evaluating "CTTrack" on VOT2020, please install VOT toolkit which is required to evaluate our tracker. To download and install VOT toolkit, you can follow this [tutorial](https://www.votchallenge.net/howto/tutorial_python.html). For convenience, you can use our example workspaces of VOT toolkit under ```external/vot20/``` by setting ```trackers.ini```.
```
cd external/vot20/
vot evaluate --workspace . CTTrackPython
# generating analysis results
vot analysis --workspace . CTTrackPython --nocache
```
## Visualize attention maps
## Model Zoo and raw results
[[Models and Raw results]](https://drive.google.com/drive/folders/1xmlOzwIu_2Jexzzl69MCPFXBip1MgSbf?usp=sharing) (Google Driver) [[Models and Raw results]](https://pan.baidu.com/s/1TCdIHfwxnlLDed98fzbyFg) (Baidu Driver: RLBW)
## Contact
Zikai Song: [email protected]
Run Luo: [email protected]
## Acknowledgments
* Thanks for [PyTracking](https://github.com/visionml/pytracking) Library, [MixFormer](https://github.com/MCG-NJU/MixFormer) Library and [MAE](https://github.com/facebookresearch/mae) Library, which helps us to quickly implement our ideas.
## Citation
If you think this project is helpful, please feel free to leave a star⭐️ and cite our paper:
```
@InProceedings{Song_2023_AAAI,
author = {Song, Zikai and Luo, Run and Yu, Junqing and Chen, Yi-Ping Phoebe and Yang, Wei},
title = {Compact Transformer Tracker with Correlative Masked Modeling},
booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
month = {February},
year = {2023}
}
```