文件名称:DLTcode
介绍说明－－下载内容均来自于网络，请自行研究使用
Robust Non-negative Dictionary Learning for Visual Tracking
The provided codes could be either embedded into the benchmark fr a mework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence.
To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m.
To run on individual video, you need to modify the dataPath and title in run_individual.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 and run_individual.m!
-Robust Non-negative Dictionary Learning for Visual Tracking
The provided codes could be either embedded into the benchmark fr a mework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence.
To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m.
To run on individual video, you need to modify the dataPath and title in run_individual.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 and run_individual.m!
The provided codes could be either embedded into the benchmark fr a mework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence.
To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m.
To run on individual video, you need to modify the dataPath and title in run_individual.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 and run_individual.m!
-Robust Non-negative Dictionary Learning for Visual Tracking
The provided codes could be either embedded into the benchmark fr a mework of paper Online Object Tracking: A Benchmark (CVPR2013) (You can find details here: http://visual-tracking.net/) or run on individual sequence.
To run the benchmark, just put the entire folder into the /trackers folder in the benchmark code base, and modify the configTrackers.m in util folder. DLT gets an AUC of 0.436, which ranks 5th among 26 in the benchmark by 19/03/2014. We don t tune parameters for single sequence in this case, all the parameters are stored in trackparam_DLT.m.
To run on individual video, you need to modify the dataPath and title in run_individual.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 and run_individual.m!
(系统自动生成,下载前可以参看下载内容)
下载文件列表
DLT
...\DLT
...\...\affineUtility
...\...\.............\affparam2geom.m
...\...\.............\affparam2mat.m
...\...\.............\affparaminv.m
...\...\.............\affwarpimg.m
...\...\drawUtility
...\...\...........\drawbox.m
...\...\...........\drawtrackresult.m
...\...\...........\showimgs.m
...\...\estwarp_condens_DLT.m
...\...\imageUtility
...\...\............\interp2.cpp
...\...\............\interp2.dll
...\...\............\interp2.mexa64
...\...\............\interp2.mexglx
...\...\............\interp2.mexw64
...\...\............\sampleNeg.m
...\...\............\samplePos_DLT.m
...\...\............\warpimg.m
...\...\initDLT.m
...\...\NN
...\...\..\nnapplygrads.m
...\...\..\nnbp.m
...\...\..\nnchecknumgrad.m
...\...\..\nneval.m
...\...\..\nnff.m
...\...\..\nnpredict.m
...\...\..\nnsetup.m
...\...\..\nntest.m
...\...\..\nntrain.m
...\...\..\nnupdatefigures.m
...\...\..\sigm.m
...\...\pretrain.mat
...\...\readme.txt
...\...\run_DLT.m
...\...\run_individual.m
...\...\trackparam_DLT.m
...\Results
...\.......\basketball_DLT.mat
...\.......\bolt_DLT.mat
...\.......\boy_DLT.mat
...\.......\car4_DLT.mat
...\.......\carDark_DLT.mat
...\.......\carScale_DLT.mat
...\.......\coke_DLT.mat
...\.......\couple_DLT.mat
...\.......\crossing_DLT.mat
...\.......\david2_DLT.mat
...\.......\david3_DLT.mat
...\.......\david_DLT.mat
...\.......\deer_DLT.mat
...\.......\dog1_DLT.mat
...\.......\doll_DLT.mat
...\.......\dudek_DLT.mat
...\.......\faceocc1_DLT.mat
...\.......\faceocc2_DLT.mat
...\.......\fish_DLT.mat
...\.......\fleetface_DLT.mat
...\.......\football1_DLT.mat
...\.......\football_DLT.mat
...\.......\freeman1_DLT.mat
...\.......\freeman3_DLT.mat
...\.......\freeman4_DLT.mat
...\.......\girl_DLT.mat
...\.......\ironman_DLT.mat
...\.......\jogging-1_DLT.mat
...\.......\jogging-2_DLT.mat
...\.......\jumping_DLT.mat
...\.......\lemming_DLT.mat
...\.......\liquor_DLT.mat
...\.......\matrix_DLT.mat
...\.......\mhyang_DLT.mat
...\.......\motorRolling_DLT.mat
...\.......\mountainBike_DLT.mat
...\.......\shaking_DLT.mat
...\.......\singer1_DLT.mat
...\.......\singer2_DLT.mat
...\.......\skating1_DLT.mat
...\.......\skiing_DLT.mat
...\.......\soccer_DLT.mat
...\.......\subway_DLT.mat
...\.......\suv_DLT.mat
...\.......\sylvester_DLT.mat
...\.......\tiger1_DLT.mat
...\.......\tiger2_DLT.mat
...\.......\trellis_DLT.mat
...\.......\walking2_DLT.mat
...\.......\walking_DLT.mat
...\.......\woman_DLT.mat