MATLAB
Need help with DCFNet?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.
foolwood

Description

DCFNet: Discriminant Correlation Filters Network for Visual Tracking

202 Stars 65 Forks 121 Commits 12 Opened issues

Services available

Need anything else?

DCFNET: DISCRIMINANT CORRELATION FILTERS NETWORK FOR VISUAL TRACKING(arXiv)

By Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu

Introduction

DCFNet

Discriminant Correlation Filters (DCF) based methods now become a kind of dominant approach to online object tracking. The features used in these methods, however, are either based on hand-crafted features like HoGs, or convolutional features trained independently from other tasks like image classification. In this work, we present an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously.

Contents

  1. Requirements
  2. Tracking
  3. Training
  4. Results
  5. Citation

Requirements

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

Requirements for MatConvNet 1.0-beta24 (see: MatConvNet)

  1. Downloading MatConvNet
cd 
git clone https://github.com/vlfeat/matconvnet.git
  1. Compiling MatConvNet

Run the following command from the MATLAB command window:

cd matconvnet
run matlab/vl_compilenn

[Optional]

If you want to reproduce the speed in our paper, please follow the website to compile the GPU version.

Tracking

The file

demo/demoDCFNet.m
is used to test our algorithm.

To reproduce the performance on OTB , you can simple copy

DCFNet/
into OTB toolkit.

[Note] Configure MatConvNet path in

tracking_env.m

Training

1.Download the training data. (VID)

2.Data Preprocessing in MATLAB.

cd training/dataPreprocessing
data_preprocessing();
analyze_data();

3.Train a DCFNet model.

train_DCFNet();

Results

DCFNet obtains a significant improvements by

  • Good Training dataset. (TC128+UAV123+NUS_PRO -> VID)
  • Good learning policy. (constant 1e-5 -> logspace(-2,-5,50))
  • Large padding size. (1.5 -> 2.0)

The OPE/TRE/SRE results on OTB BaiduYun or GoogleDrive.

result on OTB

Citing DCFNet

If you find DCFNet useful in your research, please consider citing:

@article{wang17dcfnet,
    Author = {Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu},
    Title = {DCFNet: Discriminant Correlation Filters Network for Visual Tracking},
    Journal = {arXiv preprint arXiv:1704.04057},
    Year = {2017}
}

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.