/DCFNet_pytorch

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

Primary LanguagePythonMIT LicenseMIT

DCFNet_pytorch(arXiv)

This repository contains a Python reimplementation of the DCFNet.

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
  2. Test
  3. Train
  4. Citing DCFNet

Requirements

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

Requirements for PyTorch 0.4.0 and opencv-python

conda install pytorch torchvision -c pytorch
conda install -c menpo opencv

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

Test

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)

    ./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.

    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 ⚡ ⚡ ⚡ ⚡)

    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.

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

  2. Search a better hyper-parameter.

    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 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}
}