https://github.com/ZikunZhou/SAOT

Official implementation for Saliency-Associated Object Tracking
https://github.com/ZikunZhou/SAOT

Last synced: 6 days ago
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Official implementation for Saliency-Associated Object Tracking

• Host: GitHub
• URL: https://github.com/ZikunZhou/SAOT
• Owner: ZikunZhou
• Created: 2021-08-06T02:42:29.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2022-08-01T11:47:06.000Z (over 2 years ago)
• Last Synced: 2024-08-02T06:13:20.859Z (3 months ago)
• Language: Python
• Homepage:
• Size: 1.81 MB
• Stars: 39
• Watchers: 5
• Forks: 5
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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• Awesome-Visual-Object-Tracking - [code

README

        
## SAOT

The official implementation of the paper [**Saliency-Associated Object Tracking**](https://arxiv.org/abs/2108.03637) (accepted by ICCV2021).

![SAOT_Framework](SAOT_framework.jpg)

### Updates

Support PyTorch1.7 and CUDA11.0.

Support DDP.

Update the training settings for better performance. 

Training and testing our model with the new settings using a single GPU, the performance on LaSOT reaches 0.631 (AUC).

### Performance reported in the paper

| Dataset | LaSOT (AUC)| GOT-10K (AO)| OTB2015 (AUC)| VOT2018 (EAO) |

|---|---|---|---|---|

|**SAOT**|**0.616**|**0.640**|**0.714**|**0.501**|

|Ocean|0.560|0.611|0.684|0.489|

|DiMP|0.568|0.611|0.686|0.440|

### Real-time Speed

SAOT runs at about **29 FPS** on an RTX 2080 GPU with PyTorch 1.1 and CUDA 10.

### Model Zoo & Raw Results

The pre-trained model and raw results are provided in [model zoo](https://drive.google.com/drive/folders/1T5F4JsZ-P-vzzUr5KXJxw853LTUO_lmb?usp=sharing) and [raw results](https://drive.google.com/drive/folders/1_x6mlr0rVbF4sUuasCgIxYINvOqqHCJq?usp=sharing), respectively. 

### Installation

#### Clone the GIT repository.  

```bash

git clone https://github.com/ZikunZhou/SAOT.git

```

   

#### Clone the submodules.  

In the repository directory, run the commands:  

```bash

git submodule update --init  

```

#### Install dependencies

Please note that using a new version pytorch may cause unexpected issues.

Following the [detailed installation instructions](INSTALL.md) to install the dependencies.

### Testing

Download the pre-trained networks.

Activate the conda environment and run the script pytracking/test_saot_fs.py.  

```bash

conda activate SAOT

cd pytracking

python test_saot_fs.py saot saot_otb --dataset OTB2015

```

### Training

Download the training datasets.

Activate the conda environment and run the script train.py.

```bash

conda activate SAOT

python train.py --train_module dimp --train_name saot --mode multiple --nproc_per_node 1

```

### Citation

Please cite the following publication, if you find the code helpful in your research.

```

@InProceedings{Zhou_2021_ICCV,

    author    = {Zhou, Zikun and Pei, Wenjie and Li, Xin and Wang, Hongpeng and Zheng, Feng and He, Zhenyu},

    title     = {Saliency-Associated Object Tracking},

    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},

    month     = {October},

    year      = {2021},

    pages     = {9866-9875}

}

```

### Acknowledgments

Thanks for the [PyTracking](https://github.com/visionml/pytracking) and [Pysot](https://github.com/STVIR/pysot.git) libraries, which helps us to quickly implement our ideas. 

Thanks for Kaige Mao for the helps about the updates.

### Contact

Please feel free to contact me (Email: [email protected]).