Results for IJCNN 2011 competition (1st stage)

Please find below all results that were submitted during the first stage of the IJCNN 2011 competition "The German Traffic Sign Recognition Benchmark" between January 19, and January 21, 2011. The top contestants won a free registration for the conference and were invited to participate in final live competition session that was held at IJCNN 2011 in San Jose, CA, USA. The results of this final session are published in the official GTSRB result table.


Team
Method
Total
Subset
[86] masaki0000
96.27%
96.27%
[88] masaki0001
96.28%
96.28%
[89] masaki0002
96.30%
96.30%
[179] shn2L NL SFA(HOG02)+RG+GC
96.40%
96.40%
[173] shn2L NL SFA(HOG2) CCC
95.19%
95.19%
[182] shn2L NL SFA(IM+HOG02)+CC
95.12%
95.11%
[123] matiko2LDA+2SVM
96.40%
96.40%
[132] matiko2LDA+2SVM (2)
96.37%
96.37%
[124] matiko2LDA+3SVM
96.44%
96.44%
[128] matiko2LDA+4SVM - various features
96.40%
96.40%
[127] matiko2LDA+5SVM
96.42%
96.42%
[129] matiko2LDA+6SVM, various features
96.44%
96.44%
[152] bilboANN~80/600 (sect. hue+moments)
38.06%
38.06%
[165] bilboANN~88/1200(sect. hue+moments)
28.63%
28.63%
[169] bilboANN~88/700s(sect. hue+moments)
42.91%
42.91%
[186] soumithCascaded ConvNet 70 feat
94.41%
94.41%
[189] soumithCascaded ConvNet Mixed arch
94.06%
94.06%
[158] soumithCascaded cscscf ConvNet
90.19%
90.19%
[40] masakiCMmodel
95.72%
95.72%
[46] masakicmmodel 2
95.72%
95.72%
[166] IDSIACNN 6HL
97.56%
97.56%
[191] IDSIACNN 7HL
98.10%
98.10%
[192] IDSIACNN 7HL newnorm
0.00%
0.02%
[193] IDSIACNN 7HL norm
98.46%
98.46%
[170] IDSIACNN(IMG)_MLP(HOG3)
98.79%
98.79%
[177] IDSIACNN(IMG)_MLP(HOG3)_MLP(HAAR)
98.72%
98.72%
[196] IDSIAcnn_cnn_hog3
98.98%
98.98%
[195] IDSIAcnn_cnn_hog3_haar
98.97%
98.97%
[197] IDSIAcnn_hog3
98.98%
98.98%
[157] noobCombined HOG+VQ+LDA
96.79%
96.79%
[36] AlcalaGRAM2Contrast Gaussian param modif
85.87%
85.87%
[30] Alcalá-GRAMContrast-Gaussian SVM
84.96%
84.96%
[98] Radu.Timofte@VISICSCS+HOGs
96.63%
96.63%
[176] Radu.Timofte@VISICSCS+HOGs+limited
97.01%
97.01%
[74] TDCCVOG + ANN (Team 3)
81.80%
81.80%
[11] TDCCVOG + CCV + NN (Team 1)
82.37%
82.37%
[12] TDCCVOG + CCV + NN (Team 2)
82.67%
82.67%
[105] TDCCVOG+HOG(set1)+ANN
91.81%
91.81%
[142] TDCCVOG+HOG+texture+ANN
94.73%
94.73%
[26] sermanetEBLearn 2-layer ConvNet ms
98.59%
98.59%
[198] sermanetEBLearn 2-layer ConvNet ms reg
98.41%
98.41%
[27] sermanetEBLearn 2-layer ConvNet ss
98.20%
98.20%
[185] sermanetEBLearn 2L CNN ms + validation
98.41%
98.41%
[187] sermanetEBLearn 2LConvNet ms 108 + val
98.89%
98.89%
[178] sermanetEBLearn 2LConvNet ms 108 feats
98.97%
98.97%
[43] masakiEnsemble
95.83%
95.83%
[45] masakiEnsemble 4
95.82%
95.82%
[49] masakiEnsemble23
91.25%
91.25%
[32] AlcalaGRAM2Gaussian SVM param selection
75.67%
75.67%
[138] noobHOG + Data Clustering
96.39%
96.39%
[140] noobHOG + Diaglinear Analysis
95.82%
95.82%
[4] INI-RTCVHOG 1 + 1-NN (Euclidean)
73.65%
73.65%
[7] INI-RTCVHOG 1 + 3-NN (Euclidean)
73.89%
73.89%
[5] INI-RTCVHOG 2 + 1-NN (Euclidean)
72.81%
72.81%
[8] INI-RTCVHOG 2 + 3-NN (Euclidean)
72.81%
72.81%
[6] INI-RTCVHOG 3 + 1-NN (Euclidean)
73.82%
73.82%
[9] INI-RTCVHOG 3 + 3-NN (Euclidean)
73.82%
73.82%
[1] INI-RTCVHOG features (Set 1) + LDA
94.51%
94.51%
[2] INI-RTCVHOG features (Set 2) + LDA
96.32%
96.32%
[3] INI-RTCVHOG features (Set 3) + LDA
94.73%
94.73%
[99] TDCHOG(set 1) + ANN
90.99%
90.99%
[136] TomatoHOG+LDA
96.53%
96.53%
[84] noobHOG+LDA+VQ
96.87%
96.87%
[143] TDCHOG+textures+ANN
91.99%
91.99%
[137] TomatoHOG0203 + LDA
96.40%
96.40%
[80] masakiHOG02s16c4w1
95.72%
95.72%
[64] masakiHOG02s4c2w1
95.72%
95.72%
[66] masakihog02s4c4w1
95.72%
95.72%
[161] testnHOG1 features + LDA
94.48%
94.48%
[135] nistorHOG1 LDA modified
0.29%
0.29%
[52] masakiHOG2
95.54%
95.54%
[106] TomatoHOG2 + matlab LDA
96.32%
96.32%
[101] noobHOG2 + VQ + Variant of LDA
96.59%
96.59%
[162] nistorHOG2 nomralize with LDA
96.31%
96.31%
[172] nistorHOG3 normalization + LDA
94.70%
94.70%
[38] nistorHOGNEW
2.04%
2.04%
[39] nistorHOGNEW2
2.04%
2.04%
[146] TomatoHOGNoScaleLDA
96.53%
96.53%
[139] TomatoHOGs+LDA
91.88%
91.88%
[104] TomatoHOG_02-L2RL2SVM
95.89%
95.89%
[108] TomatoHOG_02_CramerAndSinger
95.85%
95.85%
[134] olbustosaHOG_SVM
76.35%
76.35%
[164] MadRoseHopfield Network
88.84%
88.84%
[167] nistorHueHist normalization and LDA
14.50%
14.50%
[199] INI-RTCVHuman performance
98.81%
98.81%
[91] masakihybird-scale23s4de-mergef8
96.07%
96.07%
[154] TDChybrid feature + ANN
94.73%
94.73%
[92] masakihybrid2
96.07%
96.07%
[93] masakihybrid3
96.28%
96.28%
[183] Radu.Timofte@VISICSIKSVM + PHOG + HOG2
97.88%
97.88%
[100] CAORKdTree + HOG 2520
92.90%
92.90%
[119] CAORKdTree + HOG 3456 kNN 5
88.27%
88.27%
[102] CAORKdTree + HOG 9720
93.21%
93.21%
[103] CAORKdTree + HOG 9720 kNN 5
93.40%
93.40%
[116] CAORKdTree HOG 2 2
68.80%
68.80%
[109] CAORKdTree HOG Set1
70.65%
70.65%
[115] CAORKdTree HOG Set3 2
70.86%
70.86%
[118] CAORKdTree HOG9720 kNN5 iter 1000
93.72%
93.72%
[117] CAORKdTree Hue
17.53%
17.53%
[122] matikoLDA+2SVM
96.38%
96.38%
[125] matikoLDA+4SVM
96.45%
96.45%
[194] matikoLDA+OVA
96.44%
96.44%
[180] matikoLDA+OVO+OVA
96.16%
96.16%
[33] masakilibsvm --s
95.82%
95.82%
[14] masakilinear SVM
3.67%
3.67%
[37] masakilinear svm --s param selection
90.39%
90.39%
[16] masakilinear SVM fixed
95.60%
95.60%
[17] masakilinear SVM fixed 2
95.60%
95.60%
[18] masakilinear SVM param selection
95.81%
95.81%
[171] titanlinearSVM_Allpairs_ensemble
94.12%
94.12%
[145] titanlinearSVM_Allpairs_HOG02
93.67%
93.67%
[147] titanlinearSVM_Allpairs_HOG02_2
93.67%
93.67%
[144] titanlinearSVM_Allpairs_HOG123
94.16%
94.16%
[168] titanlinearSVM_ECOC_ensemble
95.90%
95.90%
[141] titanlinearSVM_ECOC_HOG02
95.70%
95.70%
[53] masakilogistic23
95.82%
95.82%
[57] masakimerge5
96.25%
96.25%
[56] masakimergeall
96.05%
96.05%
[55] masakimergef - mix, logis,23 cm2
96.20%
96.20%
[65] masakimergef3
96.20%
96.20%
[67] masakimergef4
96.28%
96.28%
[68] masakimergef5
96.31%
96.31%
[79] masakimergef6
96.20%
96.20%
[81] masakimergef7
96.24%
96.24%
[83] masakimergef8
96.29%
96.29%
[85] masakimergeff
96.28%
96.28%
[48] Brainsignalsmet1
94.61%
94.61%
[50] masakimix23
95.78%
95.78%
[54] masakimix23scale
96.07%
96.07%
[181] FiVENiNEsMulticlass SVM with CV preproc
74.83%
74.83%
[188] italian_crashMultiDataset Alg Final
95.35%
95.35%
[175] italian_crashMultiDataset Alg Fix 5
96.78%
96.78%
[149] italian_crashMultiDataset Alg Fix1
95.34%
95.34%
[150] italian_crashmultiDataset Alg Fix2
95.28%
95.28%
[156] italian_crashMultiDataset Alg Fix3
95.48%
95.48%
[159] italian_crashMultiDataset Alg Fix4
91.16%
91.16%
[160] italian_crashMultiDataset Alg Fix4.1
91.03%
91.03%
[72] italian_crashMultiDataset Algorithm
83.08%
83.08%
[107] olbustosaNaiveBayes
74.64%
74.64%
[133] olbustosaNaiveBayesMultinomial
73.94%
73.94%
[10] noobNearest Subspace
87.70%
87.70%
[13] noobNearest Subspace 1
18.77%
18.77%
[75] noobNearest Subspace 2
86.18%
86.18%
[15] Diego Peteiro-BarralOptimal 1-layer ANN
95.47%
95.47%
[19] Diego Peteiro-BarralOptimal 1-layer ANN (2)
95.57%
95.57%
[22] Diego Peteiro-BarralOptimal 1-layer ANN (3)
95.47%
95.47%
[23] Diego Peteiro-BarralOptimal 1-layer ANN (4)
95.57%
95.57%
[24] Diego Peteiro-BarralOptimal 1-layer ANN (5)
95.47%
95.47%
[174] CAORRandom trees HOG3
92.13%
92.13%
[114] olbustosaRandomForest-Hog
83.24%
83.24%
[130] olbustosaRandomForest_HOG_100tree
94.18%
94.18%
[121] olbustosaRandomForest_Hog_20tree
91.34%
91.34%
[148] olbustosaRandomForest_Hog_Hue_100tree
94.47%
94.47%
[151] olbustosaRandomForest_hog_hue_200tree
95.05%
95.05%
[47] olbustosaRandomForest_ini
64.15%
64.15%
[51] masakireduced2 svm
95.24%
95.24%
[95] masakiscale23s4c4
96.29%
96.29%
[82] masakiscale23s4c_5
96.29%
96.29%
[78] masakiscale23s4de
96.29%
96.29%
[63] masakiscale23vote-s0
95.73%
95.73%
[34] shnSecond Network CC
89.70%
89.70%
[190] shnSFA(SFA(IM)+HOG)
95.16%
95.16%
[155] Brainsignalssimple alignment plus htsa
94.24%
94.24%
[96] nistorsMsup
2.32%
2.32%
[35] nistorSOM CL
3.55%
3.55%
[29] nistorSOMCL
1.93%
1.93%
[184] Radu.Timofte@VISICSSRC + LDAs I/HOG1/HOG2
97.35%
97.35%
[58] noobSubspace Analysis 1
88.54%
88.54%
[76] noobSubspace Analysis 10
96.42%
96.42%
[77] noobSubspace Analysis 11
96.61%
96.61%
[87] noobSubspace Analysis 13
96.73%
96.73%
[90] noobSubspace Analysis 14
96.65%
96.65%
[59] noobSubspace Analysis 2
72.71%
72.71%
[60] noobSubspace Analysis 3
91.98%
91.98%
[61] noobSubspace Analysis 4
92.09%
92.09%
[62] noobSubspace Analysis 5
91.57%
91.57%
[69] noobSubspace Analysis 6
94.84%
94.84%
[70] noobSubspace Analysis 7
96.67%
96.67%
[71] noobSubspace Analysis 8
95.47%
95.47%
[73] noobSubspace Analysis 9
96.74%
96.74%
[31] RMULGSubwindows+ET
67.43%
67.43%
[42] RMULGSubwindows+ETGRAY
74.80%
74.80%
[97] RMULGSubwindows+ETGRAY+LIBLINEAR
79.71%
79.71%
[41] RMULGSubwindows+ETK28
59.64%
59.64%
[163] RMULGSubwindows+Filters+ET+LIBLINEA
76.31%
76.31%
[21] shntest CC
87.02%
87.02%
[20] shntest GC
86.62%
86.62%
[153] TDCVoting+16ANN
48.25%
48.25%
[94] masakiwhybrid1
96.34%
96.34%
[44] masakizeroLinearSVM
95.47%
95.47%

References

[1] HOG features (Set 1) + LDA, INI-RTCV

[2] HOG features (Set 2) + LDA, INI-RTCV

[3] HOG features (Set 3) + LDA, INI-RTCV

[4] HOG 1 + 1-NN (Euclidean), INI-RTCV

[5] HOG 2 + 1-NN (Euclidean), INI-RTCV

[6] HOG 3 + 1-NN (Euclidean), INI-RTCV

[7] HOG 1 + 3-NN (Euclidean), INI-RTCV

[8] HOG 2 + 3-NN (Euclidean), INI-RTCV

[9] HOG 3 + 3-NN (Euclidean), INI-RTCV

[10] Nearest Subspace, noob

[11] CVOG + CCV + NN (Team 1), TDC

[12] CVOG + CCV + NN (Team 2), TDC

[13] Nearest Subspace 1, noob

[14] linear SVM, masaki

[15] Optimal 1-layer ANN, Diego Peteiro-Barral

[16] linear SVM fixed, masaki

[17] linear SVM fixed 2, masaki

[18] linear SVM param selection, masaki

[19] Optimal 1-layer ANN (2), Diego Peteiro-Barral

[20] test GC, shn

[21] test CC, shn

[22] Optimal 1-layer ANN (3), Diego Peteiro-Barral

[23] Optimal 1-layer ANN (4), Diego Peteiro-Barral

[24] Optimal 1-layer ANN (5), Diego Peteiro-Barral

[26] EBLearn 2-layer ConvNet ms, sermanet

[27] EBLearn 2-layer ConvNet ss, sermanet

[29] SOMCL, nistor

[30] Contrast-Gaussian SVM, Alcalá-GRAM

[31] Subwindows+ET, RMULG

[32] Gaussian SVM param selection, AlcalaGRAM2

[33] libsvm --s, masaki

[34] Second Network CC, shn

[35] SOM CL, nistor

[36] Contrast Gaussian param modif, AlcalaGRAM2

[37] linear svm --s param selection, masaki

[38] HOGNEW, nistor

[39] HOGNEW2, nistor

[40] CMmodel, masaki

[41] Subwindows+ETK28, RMULG

[42] Subwindows+ETGRAY, RMULG

[43] Ensemble, masaki

[44] zeroLinearSVM, masaki

[45] Ensemble 4, masaki

[46] cmmodel 2, masaki

[47] RandomForest_ini, olbustosa

[48] met1, Brainsignals

[49] Ensemble23, masaki

[50] mix23, masaki

[51] reduced2 svm, masaki

[52] HOG2, masaki

[53] logistic23, masaki

[54] mix23scale, masaki

[55] mergef - mix, logis,23 cm2, masaki

[56] mergeall, masaki

[57] merge5, masaki

[58] Subspace Analysis 1, noob

[59] Subspace Analysis 2, noob

[60] Subspace Analysis 3, noob

[61] Subspace Analysis 4, noob

[62] Subspace Analysis 5, noob

[63] scale23vote-s0, masaki

[64] HOG02s4c2w1, masaki

[65] mergef3, masaki

[66] hog02s4c4w1, masaki

[67] mergef4, masaki

[68] mergef5, masaki

[69] Subspace Analysis 6, noob

[70] Subspace Analysis 7, noob

[71] Subspace Analysis 8, noob

[72] MultiDataset Algorithm, italian_crash

[73] Subspace Analysis 9, noob

[74] CVOG + ANN (Team 3), TDC

[75] Nearest Subspace 2, noob

[76] Subspace Analysis 10, noob

[77] Subspace Analysis 11, noob

[78] scale23s4de, masaki

[79] mergef6, masaki

[80] HOG02s16c4w1, masaki

[81] mergef7, masaki

[82] scale23s4c_5, masaki

[83] mergef8, masaki

[84] HOG+LDA+VQ, noob

[85] mergeff, masaki

[86] 0000, masaki

[87] Subspace Analysis 13, noob

[88] 0001, masaki

[89] 0002, masaki

[90] Subspace Analysis 14, noob

[91] hybird-scale23s4de-mergef8, masaki

[92] hybrid2, masaki

[93] hybrid3, masaki

[94] whybrid1, masaki

[95] scale23s4c4, masaki

[96] sMsup, nistor

[97] Subwindows+ETGRAY+LIBLINEAR, RMULG

[98] CS+HOGs, Radu.Timofte@VISICS

[99] HOG(set 1) + ANN, TDC

[100] KdTree + HOG 2520, CAOR

[101] HOG2 + VQ + Variant of LDA, noob

[102] KdTree + HOG 9720, CAOR

[103] KdTree + HOG 9720 kNN 5, CAOR

[104] HOG_02-L2RL2SVM, Tomato

[105] CVOG+HOG(set1)+ANN, TDC

[106] HOG2 + matlab LDA, Tomato

[107] NaiveBayes, olbustosa

[108] HOG_02_CramerAndSinger, Tomato

[109] KdTree HOG Set1, CAOR

[114] RandomForest-Hog, olbustosa

[115] KdTree HOG Set3 2, CAOR

[116] KdTree HOG 2 2, CAOR

[117] KdTree Hue, CAOR

[118] KdTree HOG9720 kNN5 iter 1000, CAOR

[119] KdTree + HOG 3456 kNN 5, CAOR

[121] RandomForest_Hog_20tree, olbustosa

[122] LDA+2SVM, matiko

[123] 2LDA+2SVM, matiko

[124] 2LDA+3SVM, matiko

[125] LDA+4SVM, matiko

[127] 2LDA+5SVM, matiko

[128] 2LDA+4SVM - various features, matiko

[129] 2LDA+6SVM, various features, matiko

[130] RandomForest_HOG_100tree, olbustosa

[132] 2LDA+2SVM (2), matiko

[133] NaiveBayesMultinomial, olbustosa

[134] HOG_SVM, olbustosa

[135] HOG1 LDA modified, nistor

[136] HOG+LDA, Tomato

[137] HOG0203 + LDA, Tomato

[138] HOG + Data Clustering, noob

[139] HOGs+LDA, Tomato

[140] HOG + Diaglinear Analysis, noob

[141] linearSVM_ECOC_HOG02, titan

[142] CVOG+HOG+texture+ANN, TDC

[143] HOG+textures+ANN, TDC

[144] linearSVM_Allpairs_HOG123, titan

[145] linearSVM_Allpairs_HOG02, titan

[146] HOGNoScaleLDA, Tomato

[147] linearSVM_Allpairs_HOG02_2, titan

[148] RandomForest_Hog_Hue_100tree, olbustosa

[149] MultiDataset Alg Fix1, italian_crash

[150] multiDataset Alg Fix2, italian_crash

[151] RandomForest_hog_hue_200tree, olbustosa

[152] ANN~80/600 (sect. hue+moments), bilbo

[153] Voting+16ANN, TDC

[154] hybrid feature + ANN, TDC

[155] simple alignment plus htsa, Brainsignals

[156] MultiDataset Alg Fix3, italian_crash

[157] Combined HOG+VQ+LDA, noob

[158] Cascaded cscscf ConvNet, soumith

[159] MultiDataset Alg Fix4, italian_crash

[160] MultiDataset Alg Fix4.1, italian_crash

[161] HOG1 features + LDA, testn

[162] HOG2 nomralize with LDA, nistor

[163] Subwindows+Filters+ET+LIBLINEA, RMULG

[164] Hopfield Network, MadRose

[165] ANN~88/1200(sect. hue+moments), bilbo

[166] CNN 6HL, IDSIA

[167] HueHist normalization and LDA, nistor

[168] linearSVM_ECOC_ensemble, titan

[169] ANN~88/700s(sect. hue+moments), bilbo

[170] CNN(IMG)_MLP(HOG3), IDSIA

[171] linearSVM_Allpairs_ensemble, titan

[172] HOG3 normalization + LDA , nistor

[173] 2L NL SFA(HOG2) CCC, shn

[174] Random trees HOG3, CAOR

[175] MultiDataset Alg Fix 5, italian_crash

[176] CS+HOGs+limited, Radu.Timofte@VISICS

[177] CNN(IMG)_MLP(HOG3)_MLP(HAAR), IDSIA

[178] EBLearn 2LConvNet ms 108 feats, sermanet

[179] 2L NL SFA(HOG02)+RG+GC, shn

[180] LDA+OVO+OVA, matiko

[181] Multiclass SVM with CV preproc, FiVENiNEs

[182] 2L NL SFA(IM+HOG02)+CC, shn

[183] IKSVM + PHOG + HOG2, Radu.Timofte@VISICS

[184] SRC + LDAs I/HOG1/HOG2, Radu.Timofte@VISICS

[185] EBLearn 2L CNN ms + validation, sermanet

[186] Cascaded ConvNet 70 feat, soumith

[187] EBLearn 2LConvNet ms 108 + val, sermanet

[188] MultiDataset Alg Final, italian_crash

[189] Cascaded ConvNet Mixed arch, soumith

[190] SFA(SFA(IM)+HOG), shn

[191] CNN 7HL, IDSIA

[192] CNN 7HL newnorm, IDSIA

[193] CNN 7HL norm, IDSIA

[194] LDA+OVA, matiko

[195] cnn_cnn_hog3_haar, IDSIA

[196] cnn_cnn_hog3, IDSIA

[197] cnn_hog3, IDSIA

[198] EBLearn 2-layer ConvNet ms reg, sermanet

[199] Human performance, INI-RTCV

Please wait while your results are processed