https://github.com/foolwood/dcfnet_pytorch

DCFNet: Discriminant Correlation Filters Network for Visual Tracking
https://github.com/foolwood/dcfnet_pytorch

cf end-to-end-learning fft pytorch tracking

Last synced: 4 days ago
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DCFNet: Discriminant Correlation Filters Network for Visual Tracking

• Host: GitHub
• URL: https://github.com/foolwood/dcfnet_pytorch
• Owner: foolwood
• License: mit
• Created: 2018-04-25T05:56:51.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2024-01-09T14:16:56.000Z (10 months ago)
• Last Synced: 2024-01-09T15:37:35.277Z (10 months ago)
• Topics: cf, end-to-end-learning, fft, pytorch, tracking
• Language: Python
• Homepage: https://arxiv.org/pdf/1704.04057.pdf
• Size: 2.49 MB
• Stars: 206
• Watchers: 12
• Forks: 60
• Open Issues: 15
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# DCFNet_pytorch([JCST](https://jcst.ict.ac.cn/en/article/doi/10.1007/s11390-023-3788-3))

[️‍🔥News️‍🔥] DCFNet is accepted in JCST. If you find [**DCFNet**](https://arxiv.org/pdf/1704.04057.pdf) useful in your research, please consider citing:

```

@Article{JCST-2309-13788,

title = {DCFNet: Discriminant Correlation Filters Network for Visual Tracking},

journal = {Journal of Computer Science and Technology},

year = {2023},

issn = {1000-9000(Print) /1860-4749(Online)},

doi = {10.1007/s11390-023-3788-3},	

author = {Wei-Ming Hu and Qiang Wang and Jin Gao and Bing Li and Stephen Maybank}

}

```

This repository contains a Python *reimplementation* of the [**DCFNet**](https://arxiv.org/pdf/1704.04057.pdf).

### Why implementation in python (PyTorch)?

- Magical **Autograd** mechanism via PyTorch. Do not need to know the complicated BP.

- Fast Fourier Transforms (**FFT**) supported by PyTorch 0.4.0. 

- Engineering demand. 

- Fast test speed (**120 FPS** on GTX 1060) and **Multi-GPUs** training.

### Contents

1. [Requirements](#requirements)

2. [Test](#test)

3. [Train](#train)

4. [Citing DCFNet](#citing-dcfnet)

## Requirements

```shell

git clone --depth=1 https://github.com/foolwood/DCFNet_pytorch

```

Requirements for **PyTorch 0.4.0** and opencv-python

```shell

conda install pytorch torchvision -c pytorch

conda install -c menpo opencv

```

Training data (VID) and Test dataset (OTB).

## Test

```shell

cd DCFNet_pytorch/track 

ln -s /path/to/your/OTB2015 ./dataset/OTB2015

ln -s ./dataset/OTB2015 ./dataset/OTB2013

cd dataset & python gen_otb2013.py

python DCFNet.py

```

## Train

1. Download training data. ([**ILSVRC2015 VID**](http://bvisionweb1.cs.unc.edu/ilsvrc2015/download-videos-3j16.php#vid)) 

   ```

   ./ILSVRC2015

   ├── Annotations

   │   └── VID├── a -> ./ILSVRC2015_VID_train_0000

   │          ├── b -> ./ILSVRC2015_VID_train_0001

   │          ├── c -> ./ILSVRC2015_VID_train_0002

   │          ├── d -> ./ILSVRC2015_VID_train_0003

   │          ├── e -> ./val

   │          ├── ILSVRC2015_VID_train_0000

   │          ├── ILSVRC2015_VID_train_0001

   │          ├── ILSVRC2015_VID_train_0002

   │          ├── ILSVRC2015_VID_train_0003

   │          └── val

   ├── Data

   │   └── VID...........same as Annotations

   └── ImageSets

       └── VID

   ```

2. Prepare training data for `dataloader`.

   ```shell

   cd DCFNet_pytorch/train/dataset

   python parse_vid.py   # save all vid info in a single json

   python gen_snippet.py  # generate snippets

   python crop_image.py  # crop and generate a json for dataloader

   ```

3. Training. (on multiple ***GPUs*** :zap: :zap: :zap: :zap:)

   ```

   cd DCFNet_pytorch/train/

   CUDA_VISIBLE_DEVICES=0,1,2,3 python train_DCFNet.py

   ```

## Fine-tune hyper-parameter

1. After training, you can simple test the model with default parameter.

   ```shell

   cd DCFNet_pytorch/track/

   python DCFNet --model ../train/work/crop_125_2.0/checkpoint.pth.tar

   ```

2. Search a better hyper-parameter.

   ```shell

   CUDA_VISIBLE_DEVICES=0 python tune_otb.py  # run on parallel to speed up searching

   python eval_otb.py OTB2013 * 0 10000

   ```

## Citing DCFNet

If you find [**DCFNet**](https://arxiv.org/pdf/1704.04057.pdf) useful in your research, please consider citing:

```

@article{wang2017dcfnet,

  title={DCFNet: Discriminant Correlation Filters Network for Visual Tracking},

  author={Wang, Qiang and Gao, Jin and Xing, Junliang and Zhang, Mengdan and Hu, Weiming},

  journal={arXiv preprint arXiv:1704.04057},

  year={2017}

}

```