Description: ICCV2013:
UDN algorithm, described in the paper the detection results, the method is the best of all the methods, and the effect is far more than other methods. Through the research of the thesis and the source code of the algorithm, the algorithm is and author also a paper method, also the algorithm do scan pictures and get the rectangular box, then by the method of rectangular box for further confirmation, and reduce the false alarm rate and false alarm rate. Another paper is: Contextual Deep Learning for Pedestrian Multi-Stage Detection
To put it bluntly, this article does not have much contribution to the pedestrian detection. Just use deep learning s CNN to do window candidate s confirmation. And the main pedestrian detection algorithm is HOG+CSS+adaboost
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File list (Check if you may need any files):
Joint Deep Learning for Pedestrian Detection\JDN_code\CNN\CDBNModel.mat
............................................\........\...\cnnapplygrads.m
............................................\........\...\cnnbp.m
............................................\........\...\cnnexamples.asv
............................................\........\...\cnnexamples.m
............................................\........\...\cnnff.m
............................................\........\...\CNNModel_init.mat
............................................\........\...\cnnsetup3.asv
............................................\........\...\cnnsetup3.m
............................................\........\...\cnntest.m
............................................\........\...\cnntrain.asv
............................................\........\...\cnntrain.m
............................................\........\...\compile.m
............................................\........\...\copycnnmodel.m
............................................\........\...\dtAccS.cc
............................................\........\...\dtAccS.mexw64
............................................\........\...\fconvn.cc
............................................\........\...\fconvn.mexw64
............................................\........\...\G.mat
............................................\........\...\GetAvgMiss.m
............................................\........\...\GetData_datareader.m
............................................\........\...\GetRegularizedW.m
............................................\........\...\GetSelWeight.m
............................................\........\...\showboxes.m
............................................\........\...\testCNNAll.m
............................................\........\...\testCNNCaltechTest2.m
............................................\........\...\testCNNCaltechTest4.asv
............................................\........\...\Testing.m
............................................\........\G.mat
............................................\........\model\CaltechTrain\CNN_CDBN_Model_iter2.mat
............................................\........\.....\INRIA\CNN_CDBN_Model_iter1.mat
............................................\........\.....\.....\CNN_CDBN_Model_iter2.mat
............................................\........\.....\.....\CNN_CDBN_Model_iter3.mat
............................................\........\.....\.....\CNN_CDBN_Model_iter4.mat
............................................\........\.....\.....\CNN_CDBN_Model_iter5.mat
............................................\........\NN\nnapplygrads.m
............................................\........\..\nnbp.m
............................................\........\..\nnchecknumgrad.m
............................................\........\..\nnexamples.m
............................................\........\..\nnff.m
............................................\........\..\nnsetup.m
............................................\........\..\nntest.m
............................................\........\..\nntrain.m
............................................\........\tmptoolbox\channels\chnsCompute.m
............................................\........\..........\........\chnsPyramid.m
............................................\........\..........\........\chnsScaling.m
............................................\........\..........\........\Contents.m
............................................\........\..........\........\convBox.m
............................................\........\..........\........\convMax.m
............................................\........\..........\........\convTri.m
............................................\........\..........\........\gradient2.m
............................................\........\..........\........\gradientHist.m
............................................\........\..........\........\gradientMag.m
............................................\........\..........\.
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