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https://github.com/foolwood/dcfnet
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
https://github.com/foolwood/dcfnet
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DCFNet: Discriminant Correlation Filters Network for Visual Tracking
- Host: GitHub
- URL: https://github.com/foolwood/dcfnet
- Owner: foolwood
- License: mit
- Created: 2016-10-27T14:57:21.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2024-01-09T14:17:07.000Z (10 months ago)
- Last Synced: 2024-06-27T04:32:24.930Z (5 months ago)
- Language: MATLAB
- Homepage:
- Size: 33.8 MB
- Stars: 214
- Watchers: 17
- Forks: 66
- Open Issues: 12
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
### DCFNET: DISCRIMINANT CORRELATION FILTERS NETWORK FOR VISUAL TRACKING([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}
}
```
### Introduction
> 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](#requirements)
2. [Tracking](#tracking)
3. [Training](#training)
4. [Results](#results)
5. [Citation](#citing-dcfnet)
## Requirements
```
git clone --depth=1 https://github.com/foolwood/DCFNet.git
```
Requirements for MatConvNet 1.0-beta24 \(see: [MatConvNet](http://www.vlfeat.org/matconvnet/install/)\)
1. Downloading MatConvNet
```
cd
git clone https://github.com/vlfeat/matconvnet.git
```
2. 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](http://www.vlfeat.org/matconvnet/install/) to compile the **GPU** version.
## Tracking
The file `demo/demoDCFNet.m` is used to test our algorithm.
To reproduce the performance on [**OTB**](http://cvlab.hanyang.ac.kr/tracker_benchmark/index.html) , you can simple copy `DCFNet/` into OTB toolkit.
[**Note**] Configure MatConvNet path in `tracking_env.m`
## Training
1.Download the training data. ([**VID**](data))
2.Data Preprocessing in MATLAB.
```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](http://pan.baidu.com/s/1boKcXkF) or [GoogleDrive](https://drive.google.com/open?id=0BwWEXCnRCqJ-SHNaYUJwaW81R1E).
## Citing DCFNet
If you find [**DCFNet**](https://arxiv.org/pdf/1704.04057.pdf) 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}
}
```