CUHKcrowd_group_profiling

Matlab code for our CVPR 2014 work "Scene-Independent Group Profiling in Crowd".
amandajshao · Jul 3, 2014 · 53b0564 · 53b0564
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diff --git a/1_8_groupSplit-festivalwalk_1_2-1/A_1_1_8_groupSplit-festivalwalk_1_2-1.mat b/1_8_groupSplit-festivalwalk_1_2-1/A_1_1_8_groupSplit-festivalwalk_1_2-1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/color_1_1_8_groupSplit-festivalwalk_1_2-1.mat b/1_8_groupSplit-festivalwalk_1_2-1/color_1_1_8_groupSplit-festivalwalk_1_2-1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/group_1_1_8_groupSplit-festivalwalk_1_2-1.mat b/1_8_groupSplit-festivalwalk_1_2-1/group_1_1_8_groupSplit-festivalwalk_1_2-1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/group_id_1_1_8_groupSplit-festivalwalk_1_2-1.mat b/1_8_groupSplit-festivalwalk_1_2-1/group_id_1_1_8_groupSplit-festivalwalk_1_2-1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/imageList.txt b/1_8_groupSplit-festivalwalk_1_2-1/imageList.txt
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diff --git a/1_8_groupSplit-festivalwalk_1_2-1/klt_3000_10_trk.txt b/1_8_groupSplit-festivalwalk_1_2-1/klt_3000_10_trk.txt
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/klt_3000_20_trk.txt b/1_8_groupSplit-festivalwalk_1_2-1/klt_3000_20_trk.txt
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/klt_3000_30_trk.txt b/1_8_groupSplit-festivalwalk_1_2-1/klt_3000_30_trk.txt
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/t_seq_1_1_8_groupSplit-festivalwalk_1_2-1.mat b/1_8_groupSplit-festivalwalk_1_2-1/t_seq_1_1_8_groupSplit-festivalwalk_1_2-1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trkClusterTimeLine_1_1_8_groupSplit-festivalwalk_1_2-1.mat b/1_8_groupSplit-festivalwalk_1_2-1/trkClusterTimeLine_1_1_8_groupSplit-festivalwalk_1_2-1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trks_1.mat b/1_8_groupSplit-festivalwalk_1_2-1/trks_1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trks_1_8_groupSplit-festivalwalk_1_2-1.mat b/1_8_groupSplit-festivalwalk_1_2-1/trks_1_8_groupSplit-festivalwalk_1_2-1.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trks_1_smooth.mat b/1_8_groupSplit-festivalwalk_1_2-1/trks_1_smooth.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trks_2.mat b/1_8_groupSplit-festivalwalk_1_2-1/trks_2.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trks_2_smooth.mat b/1_8_groupSplit-festivalwalk_1_2-1/trks_2_smooth.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trks_3.mat b/1_8_groupSplit-festivalwalk_1_2-1/trks_3.mat
diff --git a/1_8_groupSplit-festivalwalk_1_2-1/trks_3_smooth.mat b/1_8_groupSplit-festivalwalk_1_2-1/trks_3_smooth.mat
diff --git a/CoheGroupDetect/CoherentTrk/CF_neighbor.m b/CoheGroupDetect/CoherentTrk/CF_neighbor.m
@@ -0,0 +1,40 @@
+function [neighborSet,correlationSet] = CF_neighbor(allXset,allVset,K)
+%SUP_NEIGHBOR Summary of this function goes here
+%   Detailed explanation goes here
+d=length(allXset);
+nPoint=size(allXset{1,1},2);
+neighborSet=cell(1,d);
+correlationSet=cell(1,d);
+
+for j=1:d
+%      curAllX=allXset{1,j};
+     curAllV=allVset{1,j};
+     curNeighborGraph=zeros(nPoint,K);
+     curCorrelationGraph=zeros(nPoint,K);
+
+
+     for i=1:nPoint
+        curAllX=allXset{1,j}(1:2,:);
+
+        curX=curAllX(:,i);
+        distance=repmat(curX,[1 nPoint])-curAllX;
+        distance=sqrt(sum(distance.^2));
+        [B,IX] = sort(distance,'ascend');   
+        for kNN=1:min(K,numel(IX))-1
+
+            if sqrt(sum(curAllV(:,i).^2))>0 && sqrt(sum(curAllV(:,IX(kNN+1)).^2)) %&&B(j+1)<dis_th
+
+                coefficient=curAllV(:,i)'*curAllV(:,IX(kNN+1));
+                coefficient=coefficient/(sqrt(sum(curAllV(:,i).^2))*sqrt(sum(curAllV(:,IX(kNN+1)).^2)));          
+                curNeighborGraph(i,kNN)=IX(kNN+1);
+                curCorrelationGraph(i,kNN)=coefficient;
+            end       
+        end
+
+     end
+     neighborSet{1,j}=curNeighborGraph;
+     correlationSet{1,j}=curCorrelationGraph;
+
+end
+
+
diff --git a/CoheGroupDetect/CoherentTrk/CF_neighbor2pair.m b/CoheGroupDetect/CoherentTrk/CF_neighbor2pair.m
@@ -0,0 +1,31 @@
+function [pairSet,correSet] = CF_neighbor2pair(neighborSet,correlationSet,d)
+
+zeroNeighborSet=neighborSet{1,1};
+nPoint=size(zeroNeighborSet,1);
+
+
+pairSet=[];
+correSet=[];
+
+for i=1:nPoint  
+
+    curIntersect=zeroNeighborSet(i,:);
+    curCorre=zeros(1,size(zeroNeighborSet,2));
+    for j=1:d
+        nextNeighborSet=neighborSet{1,j};
+        nextCorreSet=correlationSet{1,j};
+        [curIntersect,ia,ib]=intersect(curIntersect,nextNeighborSet(i,:)); % the intersection of neighborhood
+        curCorre=curCorre(1,ia)+nextCorreSet(i,ib);
+    end   
+
+    if ~isempty(curIntersect)
+
+
+        correSet=[correSet curCorre./d];
+        pairSet=[pairSet [i*ones(1,length(curIntersect));curIntersect]];  
+    end
+end
+
+
+end
+
diff --git a/CoheGroupDetect/CoherentTrk/CF_trk2XV.m b/CoheGroupDetect/CoherentTrk/CF_trk2XV.m
@@ -0,0 +1,39 @@
+function [allXset,allVset] = CF_trk2XV(trks,curTime,d)
+%SUP_TRK2XV Summary of this function goes here
+%   Detailed explanation goes here
+% sampleTrk=trks(1,1);
+% nDimension=length(fieldnames(sampleTrk))-1;
+
+allXset=cell(1,d);
+allVset=cell(1,d);
+
+for i=1:d
+    allXset{1,i}=[];
+    allVset{1,i}=[];
+end
+
+for i=1:length(trks)
+    curStart=trks(i).t(1);curEnd=trks(i).t(end);
+    if curTime>curStart && curEnd>=(curTime+d) && length(trks(i).x)>=(curTime+d-curStart)
+%         if nDimension==2
+
+            curX=[trks(i).x((curTime-curStart):(curTime-curStart)+d-1)';trks(i).y((curTime-curStart):(curTime-curStart)+d-1)'];
+            curV=[trks(i).x((curTime-curStart)+1:(curTime-curStart)+d)' - trks(i).x((curTime-curStart):(curTime-curStart)+d-1)';...
+                trks(i).y((curTime-curStart)+1:(curTime-curStart)+d)' - trks(i).y((curTime-curStart):(curTime-curStart)+d-1)'];
+%         else
+%             
+%             curX=[trks(i).x((curTime-curStart):(curTime-curStart)+d-1)';trks(i).y((curTime-curStart):(curTime-curStart)+d-1)';trks(i).z((curTime-curStart):(curTime-curStart)+d-1)'];
+%             curV=[trks(i).x((curTime-curStart)+1:(curTime-curStart)+d)'-trks(i).x((curTime-curStart):(curTime-curStart)+d-1)';trks(i).y((curTime-curStart)+1:(curTime-curStart)+d)'-trks(i).y((curTime-curStart):(curTime-curStart)+d-1)';trks(i).z((curTime-curStart)+1:(curTime-curStart)+d)'-trks(i).z((curTime-curStart):(curTime-curStart)+d-1)'];        
+%         end
+        for j=1:d           
+            curX_temp = [curX(:,j); i];
+            allXset{1,j}=[allXset{1,j},curX_temp];
+            allVset{1,j}=[allVset{1,j},curV(:,j)];
+        end
+
+    end
+end
+
+
+end
+
diff --git a/CoheGroupDetect/CoherentTrk/CoherentFilter.m b/CoheGroupDetect/CoherentTrk/CoherentFilter.m
@@ -0,0 +1,26 @@
+function [curAllX,clusterIndex]=CoherentFilter(trks,curTime,d,K,lamda)
+%% Algorithm 1 of Coherent Filtering: Detecting coherent motion patterns at each frame
+% Last revised by Bolei Zhou Jan.24,2013
+
+%% step1: find K nearest neighbor set at each time
+
+[allXset,allVset] = CF_trk2XV(trks,curTime,d);
+curAllX=allXset{1,1};
+nPoint=size(curAllX,2);
+% display('find K nearest neighbor set at each time...');
+[neighborSet,correlationSet] = CF_neighbor(allXset,allVset,K);
+
+%% step2: find the invariant neighbor and pairwise connection set
+
+% display('search invariant neighbor and construct the pairwise connections...');
+[pairSet,correSet] = CF_neighbor2pair(neighborSet,correlationSet,d);
+
+%% step3: threshold pairwise connection set by the averaged correlation values, then generate cluster components 
+
+% display('threshold the average velocity correlations, and get the clustering...');
+pairIndex=find(correSet>lamda); % included pairwise connection
+clusterIndex=pair2cluster(pairSet(:,pairIndex),nPoint);
+
+
+end
+
diff --git a/CoheGroupDetect/CoherentTrk/pair2cluster.m b/CoheGroupDetect/CoherentTrk/pair2cluster.m
@@ -0,0 +1,43 @@
+
+function clusterIndex=pair2cluster(pairwiseData,totalNum)
+
+clusterNum=0;
+clusterIndex=zeros(1,totalNum);
+
+for i=1:size(pairwiseData,2)
+    curPair=pairwiseData(:,i);
+    curPairAlabel=clusterIndex(1,curPair(1));
+    curPairBlabel=clusterIndex(1,curPair(2));
+
+    if curPairAlabel==0 && curPairBlabel==0
+        clusterNum=clusterNum+1;
+        curPairLabel=clusterNum;
+        clusterIndex(1,curPair(1))=curPairLabel;
+        clusterIndex(1,curPair(2))=curPairLabel;
+    elseif curPairAlabel~=0 && curPairBlabel==0
+        clusterIndex(1,curPair(2))=curPairAlabel;
+    elseif curPairBlabel~=0 && curPairAlabel==0
+        clusterIndex(1,curPair(1))=curPairBlabel;
+    else
+        combineLabel=min(curPairAlabel,curPairBlabel);
+        clusterIndex(1,find(clusterIndex==curPairAlabel))=combineLabel;
+        clusterIndex(1,find(clusterIndex==curPairBlabel))=combineLabel;
+    end
+
+
+end
+
+newClusterNum=0;
+for i=1:max(clusterNum)
+    curClusterIndex=find(clusterIndex==i);
+    if length(curClusterIndex)<5
+        clusterIndex(curClusterIndex)=0;
+
+    else
+        newClusterNum=newClusterNum+1;
+        clusterIndex(curClusterIndex)=newClusterNum;
+    end
+end
+
+
+end
diff --git a/CoheGroupDetect/KalmanFilter/KPMstats/#histCmpChi2.m# b/CoheGroupDetect/KalmanFilter/KPMstats/#histCmpChi2.m#
@@ -0,0 +1,12 @@
+function d = histCmpChi2(h1, h2)
+% Compare two histograms using chi-squared
+% function d = histCmpChi2(h1, h2)
+%
+% d(i,j) = chi^2(h1(i,:), h2(j,:)) = sum_b  (h1(b
+
+[N B] = size(h1);
+d = zeros(N,N);
+for i=1:N
+  hh1 = repmat(h1(i,:), N, 1);
+  numer = (hh1 .- h2).^2;
+
diff --git a/CoheGroupDetect/KalmanFilter/KPMstats/CVS/Entries b/CoheGroupDetect/KalmanFilter/KPMstats/CVS/Entries
@@ -0,0 +1,73 @@
+/KLgauss.m/1.1/Tue Apr 26 02:29:16 2005//
+/README.txt/1.1/Tue Apr 26 02:29:17 2005//
+/beta_sample.m/1.1/Tue Apr 26 02:29:17 2005//
+/chisquared_histo.m/1.1/Tue Apr 26 02:29:17 2005//
+/chisquared_prob.m/1.1/Tue Apr 26 02:29:17 2005//
+/chisquared_readme.txt/1.1/Tue Apr 26 02:29:17 2005//
+/chisquared_table.m/1.1/Tue Apr 26 02:29:17 2005//
+/clg_Mstep.m/1.1/Tue Apr 26 02:29:17 2005//
+/clg_Mstep_simple.m/1.1/Tue Apr 26 02:29:17 2005//
+/clg_prob.m/1.1/Tue Apr 26 02:29:17 2005//
+/condGaussToJoint.m/1.1/Tue Apr 26 02:29:17 2005//
+/cond_indep_fisher_z.m/1.1/Tue Apr 26 02:29:17 2005//
+/condgaussTrainObserved.m/1.1/Tue Apr 26 02:29:17 2005//
+/condgauss_sample.m/1.1/Tue Apr 26 02:29:17 2005//
+/convertBinaryLabels.m/1.1/Tue Apr 26 02:29:17 2005//
+/cwr_demo.m/1.1/Tue Apr 26 02:29:18 2005//
+/cwr_em.m/1.1/Tue Apr 26 02:29:18 2005//
+/cwr_predict.m/1.1/Tue Apr 26 02:29:18 2005//
+/cwr_prob.m/1.1/Tue Apr 26 02:29:18 2005//
+/cwr_readme.txt/1.1/Tue Apr 26 02:29:18 2005//
+/cwr_test.m/1.1/Tue Apr 26 02:29:18 2005//
+/dirichlet_sample.m/1.1/Tue Apr 26 02:29:18 2005//
+/distchck.m/1.1/Tue Apr 26 02:29:18 2005//
+/eigdec.m/1.1/Tue Apr 26 02:29:18 2005//
+/est_transmat.m/1.1/Tue Apr 26 02:29:18 2005//
+/fit_paritioned_model_testfn.m/1.1/Tue Apr 26 02:29:18 2005//
+/fit_partitioned_model.m/1.1/Tue Apr 26 02:29:18 2005//
+/gamma_sample.m/1.1/Tue Apr 26 02:29:18 2005//
+/gaussian_prob.m/1.1/Tue Apr 26 02:29:18 2005//
+/gaussian_sample.m/1.1/Tue Apr 26 02:29:18 2005//
+/linear_regression.m/1.1/Tue Apr 26 02:29:18 2005//
+/logist2.m/1.1/Tue Apr 26 02:29:19 2005//
+/logist2Apply.m/1.1/Tue Apr 26 02:29:19 2005//
+/logist2ApplyRegularized.m/1.1/Tue Apr 26 02:29:19 2005//
+/logist2Fit.m/1.1/Tue Apr 26 02:29:19 2005//
+/logist2FitRegularized.m/1.1/Tue Apr 26 02:29:19 2005//
+/logistK.m/1.1/Tue Apr 26 02:29:19 2005//
+/logistK_eval.m/1.1/Tue Apr 26 02:29:19 2005//
+/marginalize_gaussian.m/1.1/Tue Apr 26 02:29:19 2005//
+/matrix_T_pdf.m/1.1/Tue Apr 26 02:29:19 2005//
+/matrix_normal_pdf.m/1.1/Tue Apr 26 02:29:19 2005//
+/mc_stat_distrib.m/1.1/Tue Apr 26 02:29:19 2005//
+/mixgauss_Mstep.m/1.1/Tue Apr 26 02:29:19 2005//
+/mixgauss_classifier_apply.m/1.1/Tue Apr 26 02:29:19 2005//
+/mixgauss_classifier_train.m/1.1/Tue Apr 26 02:29:19 2005//
+/mixgauss_em.m/1.1/Tue Apr 26 02:29:19 2005//
+/mixgauss_init.m/1.1/Tue Apr 26 02:29:19 2005//
+/mixgauss_prob.m/1.1/Tue Apr 26 02:29:20 2005//
+/mixgauss_prob_test.m/1.1/Tue Apr 26 02:29:20 2005//
+/mixgauss_sample.m/1.1/Tue Apr 26 02:29:20 2005//
+/mkPolyFvec.m/1.1/Tue Apr 26 02:29:20 2005//
+/mk_unit_norm.m/1.1/Tue Apr 26 02:29:20 2005//
+/multinomial_prob.m/1.1/Tue Apr 26 02:29:20 2005//
+/multinomial_sample.m/1.1/Tue Apr 26 02:29:20 2005//
+/normal_coef.m/1.1/Tue Apr 26 02:29:20 2005//
+/partial_corr_coef.m/1.1/Tue Apr 26 02:29:20 2005//
+/parzen.m/1.1/Tue Apr 26 02:29:20 2005//
+/parzenC.c/1.1/Tue Apr 26 02:29:20 2005//
+/parzenC.dll/1.1/Tue Apr 26 02:29:20 2005/-kb/
+/parzenC.mexglx/1.1/Tue Apr 26 02:29:20 2005/-kb/
+/parzenC_test.m/1.1/Tue Apr 26 02:29:20 2005//
+/parzen_fit_select_unif.m/1.1/Tue Apr 26 02:29:20 2005//
+/pca.m/1.1/Tue Apr 26 02:29:20 2005//
+/rndcheck.m/1.1/Tue Apr 26 02:29:21 2005//
+/sample.m/1.1/Tue Apr 26 02:29:21 2005//
+/sample_discrete.m/1.1/Tue Apr 26 02:29:21 2005//
+/sample_gaussian.m/1.1/Tue Apr 26 02:29:21 2005//
+/standardize.m/1.1/Tue Apr 26 02:29:21 2005//
+/student_t_logprob.m/1.1/Tue Apr 26 02:29:21 2005//
+/student_t_prob.m/1.1/Tue Apr 26 02:29:21 2005//
+/unif_discrete_sample.m/1.1/Tue Apr 26 02:29:21 2005//
+/weightedRegression.m/1.1/Tue Apr 26 02:29:21 2005//
+D
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+Diff line change
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