文件列表:

DLT (0, 2013-10-28)
DLT\affineUtility (0, 2013-10-26)
DLT\affineUtility\affparam2geom.m (1206, 2008-05-04)
DLT\affineUtility\affparam2mat.m (1121, 2008-05-04)
DLT\affineUtility\affparaminv.m (452, 2008-05-04)
DLT\affineUtility\affwarpimg.m (595, 2008-05-04)
DLT\drawUtility (0, 2013-10-26)
DLT\drawUtility\drawbox.m (1486, 2012-11-27)
DLT\drawUtility\drawtrackresult.m (3989, 2013-01-29)
DLT\drawUtility\showimgs.m (1519, 2013-05-10)
DLT\estwarp_condens_DLT.m (1205, 2013-10-28)
DLT\imageUtility (0, 2013-10-28)
DLT\imageUtility\interp2.cpp (4152, 2008-05-04)
DLT\imageUtility\interp2.dll (24576, 2008-05-04)
DLT\imageUtility\interp2.mexa64 (12982, 2008-05-04)
DLT\imageUtility\interp2.mexglx (10719, 2008-05-04)
DLT\imageUtility\interp2.mexw64 (9728, 2013-10-14)
DLT\imageUtility\sampleNeg.m (637, 2013-10-28)
DLT\imageUtility\samplePos_DLT.m (431, 2013-05-15)
DLT\imageUtility\warpimg.m (613, 2012-12-01)
DLT\initDLT.m (562, 2013-10-28)
DLT\NN (0, 2013-10-28)
DLT\NN\nnapplygrads.m (735, 2013-10-28)
DLT\NN\nnbp.m (2582, 2013-05-14)
DLT\NN\nnchecknumgrad.m (700, 2013-03-11)
DLT\NN\nneval.m (772, 2013-03-11)
DLT\NN\nnff.m (2199, 2013-10-28)
DLT\NN\nnpredict.m (188, 2013-03-11)
DLT\NN\nnsetup.m (2240, 2013-10-28)
DLT\NN\nntest.m (180, 2013-03-11)
DLT\NN\nntrain.m (3205, 2013-10-28)
DLT\NN\nnupdatefigures.m (2596, 2013-03-11)
DLT\NN\sigm.m (48, 2013-03-11)
DLT\pretrain.mat (22030834, 2013-10-27)
DLT\runtracker_DLT.m (2745, 2013-10-28)
DLT\trackparam_DLT.m (3838, 2013-10-28)
DLT\woman_dlt.mat (1669, 2013-10-28)

Thanks for interesting in our work "Online Robust Non-negative Dictionary Learning for Visual Tracking", Naiyan Wang and Dit-Yan Yeung, NIPS2013 If you have any problems with the codes or find bugs in codes, please contact winsty@gmail.com. To run the code, you need to modify the dataPath in trackparam_DLT.m, and then run runtracker_DLT.m If you run MATLAB version after 2012, and have a CUDA compatible GPU installed, you may enjoy the fast computation speed by GPU, just set useGPU to true in trackparam_DLT.m! The codes are built on the "Incremental Visual Tracking" codes provided by David Ross. Also the codes related to training of neural network, including offline training and online adaptation are modified from the DeepLearning Toolbox from: https://github.com/rasmusbergpalm/DeepLearnToolbox Thanks for the authors' sharing!

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