ADRNet

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code and results for 'Learning Adaptive Attribute-Driven Representation for Real-Time RGB-T Tracking'

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- generate_EI_GTOT.py
- generate_MB_GTOT.py
- generate_OCC_GTOT.py
- generate_TC_GTOT.py
- - LICENSE
  - README.md
- bbreg.py
- bbreg.pyc
- data_prov.py
- img_cropper.py
- img_cropper.pyc
- model.py
- model.pyc
- prepro_data_GTOT.py
- prepro_data_RGBT234.py
- pretrain_options.py
- sample_generator.py
- utils.py
- utils.pyc
ADRNet_bibtex
demo.zip
Framework.png
install.sh
options.py
README.md
rename_txt.py
results_raw.zip
Run_GTOT.py
Run_RGBT234.py
Run_test.py
tracker.py
train_ADRNet.py
Zhang_IJCV2021_ADRNet.pdf

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# ADRNet
The official code and results for IJCV paper: [Learning Adaptive Attribute-Driven Representation for Real-Time RGB-T Tracking](https://github.com/zhang-pengyu/ADRNet/blob/main/Zhang_IJCV2021_ADRNet.pdf)

## Framework
![avatar](https://github.com/zhang-pengyu/ADRNet/blob/main/Framework.png)

- **Attribute-Driven Residual Branch (ADRB)** aims to build a robust and discriminative appearance model when RGB or T modality is not reliable for tracking. Four heterogeneous attributes, including extreme illumination (EI), occlusion (OCC), motion blur (MB), and thermal crossover (TC), are labeled.
- **Attribute Ensemble Network (AENet)** aggregates the residual features from different attributes in channel and spatial levels, which consists of two subnetworks: Channel Ensemble Network (CENet) and Spatial Ensemble Network (SENet).

## Experiments
### Comparison with SOTA on GTOT and RGBT234.
| Tracker | GTOT(MSR/MPR) | RGBT234(MSR/MPR)|
| ------ | ------ | ------ |
| **ADRNet** | **73.9/90.4** | **57.1/80.9** |
| CAT(ECCV 20') | 71.7/88.9 | 56.1/80.4 |
| MaCNet(Sensors 20') | 71.4/88.0 | 55.4/79.0 |
| MANet(ICCVW 19') | 72.4/89.4 | 53.9/77.7 |
| DAPNet(ACM MM 19') | 70.7/88.2 | 53.7/76.6 |

### Comparison with VOT2019-RGBT competitors.
| Tracker | EAO | Acc | R |
| ------ | ------ | ------ | ------ |
| JMMAC | 0.4826 | 0.6649 | 0.8211 |
| **ADRNet** | 0.3959 | 0.6218 | 0.7567 |
| SiamDW-T | 0.3925 | 0.6158 | 0.7839 | 
| mfDiMP | 0.3879 | 0.6019 | 0.8036 |
| FSRPN | 0.3553 | 0.6362 | 0.7069 |
| MANet | 0.3463 | 0.5823 | 0.7010 |
| MPAT | 0.3180 | 0.5723 | 0.7242 |
| CISRDCF | 0.2923 | 0.5215 | 0.6904 |
| GESBTT | 0.2896 | 0.6163 | 0.6350 |

## Get Started
### Set up Anaconda environment
```
conda create -n ADRNet python=3.7
conda activate ADRNet
cd $Path_to_ADRNet$
bash install.sh
```
### Run Demo sequence
```
cd $Path_to_ADRNet$
unzip demp.zip
python Run_test.py
```
### Run RGBT234 and GTOT
```
cd $Path_to_ADRNet$
python Run_RGBT234.py
python Run_GTOT.py
```
## Training
For training ADRB, you should generate attribute-specific data via
```
cd $Path_to_ADRNet/data_generation$
python generate_EI_GTOT.py
python generate_MB_GTOT.py
python generate_OCC_GTOT.py
python generate_TC_GTOT.py
```
Then, generate pkl files via, 
```
cd $Path_to_ADRNet/modules$
python prepro_data_GTOT.py
python prepro_data_RGBT234.py
```
Finally, you can train the model after setting your data path,
```
cd $Path_to_ADRNet$
python train_ADRNet.py
```
## Model zoo
The model can be found in [google drive](https://drive.google.com/drive/folders/1u-GphvXxeI8YxOE1_TvC31HZAtSLj0yo?usp=sharing) and [baidu disk(code:56cu)](https://pan.baidu.com/s/1nVkh-69EFeydgyvc8AAvUw). After downloading, you should put it in $Path_to_ADRNet/models/$
## Citation
If you feel our work is useful, please cite,

@article{Zhang_IJCV21_ADRNet,\
	author = {Pengyu Zhang and Dong Wang and Huchuan Lu and Xiaoyun Yang},\
	title = {Learning Adaptive Attribute-Driven Representation for Real-Time RGB-T Tracking},\
	journal = IJCV,\
	volume = {129},\
	pages = {2714–2729},\
	year = {2021}\
}\
If you have any questions, feel free to contract with [me](mailto:pyzhang@mail.dlut.edu.cn)

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