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DCFNET: DISCRIMINANT CORRELATION FILTERS NETWORK FOR VISUAL TRACKING<sub>(JCST)</sub>

[️‍🔥News️‍🔥] DCFNet is accepted in JCST. If you find DCFNet 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}
}

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.

Requirements
Tracking
Training
Results
Citation

Requirements

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

Requirements for MatConvNet 1.0-beta24 (see: MatConvNet)

Downloading MatConvNet

cd <DCFNet>
git clone https://github.com/vlfeat/matconvnet.git

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}
}