Person re-identification accuracy can be significantly improved given a training set that demonstrates changes in appearances associated with the two non-overlapping cameras involved. Here we test whether this advantage can be maintained when directly annotated training sets are not available for all camera-pairs at the site. Given the training sets capturing correspondences between cameras A and B and a different training set capturing correspondences between cameras B and C, the Transitive Re-IDentification algorithm (TRID) suggested here provides a classifier for (A,C) appearance pairs. The proposed method is based on statistical modeling and uses a marginalization process for the inference. This approach significantly reduces the annotation effort inherent in a learning system, which goes down O(N^2) to O(N), for a site containing N cameras. Moreover, when adding camera (N+1), only one inter-camera training set is required for establishing all correspondences. In our experiments w

Filename	Size	Update
TRID_BMVC2013_Code_Final_withoutImages\MATLAB Code
......................................\...........\batch_SAIVTSoft_DB_AdoptedForSimulation_folder_creation.m
......................................\...........\CalcIntg2.m
......................................\...........\ConvertNum2_kDigitStr2.m
......................................\...........\CreateSingleImageFeatureVector.m
......................................\...........\CreateSuperFVs.m
......................................\...........\disptime.m
......................................\...........\doTest_ICT.m
......................................\...........\doTest_TRID2.m
......................................\...........\doTrain_ICT.m
......................................\...........\doTrain_ICT_Naive.m
......................................\...........\FASTSVM_innerPredict.mexw32
......................................\...........\findIndImageInStruct.m
......................................\...........\GetSubIndForCamCombination.m
......................................\...........\ICT.m
......................................\...........\ICT_Naive.m
......................................\...........\libsvm.dll
......................................\...........\libsvmread.mexa64
......................................\...........\libsvmread.mexw32
......................................\...........\libsvmread.mexw32 3.11
......................................\...........\libsvmread.mexw64
......................................\...........\libsvmwrite.mexa64
......................................\...........\libsvmwrite.mexw32
......................................\...........\libsvmwrite.mexw32 3.11
......................................\...........\libsvmwrite.mexw64
......................................\...........\ListImagesForExperiment.mat
......................................\...........\main_with_gui.m
......................................\...........\mysvmpredict.m
......................................\...........\mysvmtrain.m
......................................\...........\new.mat
......................................\...........\normalize.m
......................................\...........\old.mat
......................................\...........\PrepareDataForCurrentExperiment.m
......................................\...........\RandomlySelect2.m
......................................\...........\ReadSAIVTSoftData2Cameras.m
......................................\...........\res.mat
......................................\...........\resNew.mat
......................................\...........\resOld.mat
......................................\...........\SAIVTSoft	Average CMC	[A B C]=[1 5 7]	TestSetsCount=2.fig
......................................\...........\SAIVTSoft	Average CMC	[A B C]=[3 5 7]	TestSetsCount=2.fig
......................................\...........\SAIVTSoftBio_CamSetUp.jpg
......................................\...........\SAIVTSoftBio_CamSetUp.png
......................................\...........\simulation_gui.fig
......................................\...........\simulation_gui.m
......................................\...........\structCombSubInd.mat
......................................\...........\superRandsample.m
......................................\...........\svmpredict 3.11.mexw64
......................................\...........\svm-predict.exe
......................................\...........\svmpredict.mexa64
......................................\...........\svmpredict.mexw32
......................................\...........\svmpredict.mexw32 3.11
......................................\...........\svmpredict.mexw64
......................................\...........\svm-scale.exe
......................................\...........\svm-toy.exe
......................................\...........\svmtrain 3.11.mexw64
......................................\...........\svm-train.exe
......................................\...........\svmtrain.mexa64
......................................\.........

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