DCFNET: DISCRIMINANT CORRELATION FILTERS NETWORK FOR VISUAL TRACKING

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

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.

Requirements: software

Requirements for MatConvNet 1.0-beta23(see: MatConvNet)

1. Downloading MatConvNet

git clone https://github.com/vlfeat/matconvnet.git

2. Compiling MatConvNet

Run the following command from the MATLAB command window:

run <matconvnet>/matlab/vl_compilenn

Tracking

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

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

To verify OTB and VOT performance, you can simple copy DCFNet/ into OTB toolkit and integrate track4vot/ to VOT toolkit.

Training

1.Download the training data.

TColor-128:[LINK]

UAV123: [GoogleDrive]

NUS_PRO:[GoogleDrive] (part1)(part2)]

It should have this basic structure

data
    |-- NUS_PRO
    |-- Temple-color-128
    |-- UAV123

2.Run training/train_cnn_dcf.m to train a model.

You can choose the network architecture by setting opts.networkType = 21(This parameter is 21 by default)

Results on OTB and VOT2015

AUC on OTB2013 and OTB2015(OPE)

VOT2015 EAO result

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}
}