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Online_RaF
affineUtility
drawUtility Add files via upload Aug 1, 2017
imageUtility
README.md
affparam2geom.m Add files via upload Aug 1, 2017
affparam2mat.m Add files via upload Aug 1, 2017
affparaminv.m
drawbox.m
drawtrackresult.m
estwarp_condens.m
get_features.m Add files via upload Aug 1, 2017
img2patch.m Add files via upload Aug 1, 2017
initial_tracking.m Add files via upload Aug 1, 2017
interp2.cpp Add files via upload Aug 1, 2017
interp2.dll Add files via upload Aug 1, 2017
interp2.mexa64
interp2.mexglx Add files via upload Aug 1, 2017
mexLasso.m
mexLasso.mexw32
mexLasso.mexw64 Add files via upload Aug 1, 2017
normalizeMat.m Add files via upload Aug 1, 2017
paraConfig_RaF.m
readme.txt Update readme.txt Aug 1, 2017
run_RaF.m
sampling.m Add files via upload Aug 1, 2017
setTrackParam.m
sklm.m Add files via upload Aug 1, 2017
tracker.m
trackparam_DLT.m
warpimg.m

README.md

This code is for the simple RaF tracker using HOG feature published in the following paper. Zhang, Le, et al. "Robust Visual Tracking Using Oblique Random Forests.", IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), Hawaii, July, 2017

The proposed RaF is oblique and can incrementally update all internal nodes in a closed-from fashion. See my homepage for more details on oblique randmoized decision tree ensemble:

https://sites.google.com/site/zhangleuestc/home

The results are slightly sensitive to updating parameters. You may obtain better results by adjusting the parameters.

As both FCNT and RaF operate in a typical particle filter framework, you can combine the tracking confidence from each particle of FCNT ( https://github.com/scott89/FCNT) and RaF to get much better results. See our paper for more details!

This is the first version of code. We appreciate any comments/suggestions. For more quetions, please contact us via zhang.le@adsc.com.sg

Zhang Le August 2017

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