By Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu
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.
git clone --depth=1 https://github.com/foolwood/DCFNet.git
Requirements for MatConvNet 1.0-beta24 (see: MatConvNet)
cd <DCFNet>
git clone https://github.com/vlfeat/matconvnet.git
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.
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
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();
DCFNet obtains a significant improvements by
The OPE/TRE/SRE results on OTB BaiduYun or GoogleDrive.
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}
}
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