🏆News: MFT got the "winning tracker" award at the VOT2018 challenge

This repository includes matlab code for reproducing the results on VOT2018 (MFT).

Multi-hierarchical Independent Correlation Filters for Visual Tracking

By Shuai Bai, Zhiqun He, Tingbing Xu, Zheng Zhu, Yuan Dong, Hongliang Bai

MFT tracker is the VOT2018 version of MHIT. It is based on correlation filtering algorithm. Firstly, we combine different multi-resolution features with continuous convolution operator~\cite{danelljan2017eco}. Secondly, we train multi-solution independently using different features and fuse multi-solutions optimally to predict target location, which drastically improves robustness. Respectively. At last, we reasonably choose different combinations of Res50, SE-Res50, Hog, CN features as our final feature to adapt to different tracking situation.

Licensed under an MIT license.

• Supported OS: the source code was tested on 64-bit CentOS Linux release 7.3.1611 (Core), and it should also be executable in other linux distributions.
• Dependencies:
• A modified version of matconvnet (included in the ./external_libs/matconvnet folder).
• autonn(https://github.com/vlfeat/autonn) is included in the (./external_libs/autonn) folder.
• MATLAB 2016b, and all different version will change a lot.
• Cuda 8.0 enabled GPUs

cd ./feature_extraction/networks

wget http://www.vlfeat.org/matconvnet/models/imagenet-resnet-50-dag.mat

wget http://www.robots.ox.ac.uk/~albanie/models/se-nets/SE-ResNet-50-mcn.mat

Setting export CUDA_CACHE_MAXSIZE=8000000000" in the ./~bash_profile so that gpuDevice(1) will take fewer time.

run demo_MFT.m()

[VOT Intergration] ./vot2018_main/MFT.m change ./tracker_MFT.m tracker_repo_path = 'your MFT path'