DCFNet_pytorch

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DCFNet: Discriminant Correlation Filters Network for Visual Tracking

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최종 버전 다운로드 (.zip)

- - gen_otb2013.py
  - OTB2015.json
- DCFNet.py
- eval_otb.py
- net.py
- net_param.mat
- param.pth
- tune_otb.py
- util.py
- - compute-image-mean.py
  - crop_image.py
  - gen_snippet.py
  - parse_vid.py
- dataset.py
- net.py
- train_DCFNet.py
.gitignore
DCFNet-JCST.pdf
LICENSE
README.md

// repository documentation

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

[️‍🔥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 <VID_path>  # 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}
}
```

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