Results for CD.net 2012

Results, all categories combined.

Warning!!!

Methods with the "(supervised method)" tag involve a supervised machine learning algorithm potentially trained on the groundtruth data used to produce the metrics reported in this page. Thus, these methods should not be compared directly with the other unsupervised methods without further investigation and careful analysis. Please refer to the original papers for more details.

Click on method name for more details.

Method Average ranking across categories Average ranking Average     Re  Average     Sp  Average   FPR  Average   FNR  Average   PWC     Average     F-Measure  Average   Precision 
SOBS [1] 24.67 23.86 0.7882 0.9818 0.0182 0.2118 2.5642 0.7159 0.7179
GMM | KaewTraKulPong [2] 26.67 23.86 0.5072 0.9947 0.0053 0.4928 3.1051 0.5904 0.8228
KDE - ElGammal [3] 28.33 31.57 0.7442 0.9757 0.0243 0.2558 3.4602 0.6719 0.6843
GMM | Zivkovic [4] 35.00 30.00 0.6964 0.9845 0.0155 0.3036 3.1504 0.6596 0.7079
Mahalanobis distance [5] 39.17 34.29 0.7607 0.9599 0.0401 0.2393 4.6631 0.6259 0.6040
Euclidean distance [6] 40.17 35.86 0.7048 0.9692 0.0308 0.2952 4.3465 0.6111 0.6223
Local-Self similarity [7] 34.33 29.57 0.9354 0.8512 0.1488 0.0646 14.2954 0.5016 0.4139
KDE - Integrated Spatio-temporal Features [8] 26.17 24.29 0.6507 0.9932 0.0068 0.3493 2.8905 0.6418 0.7663
PSP-MRF [9] 18.33 20.14 0.8037 0.9830 0.0170 0.1963 2.3937 0.7372 0.7512
RMoG (Region-based Mixture of Gaussians) [35] 22.33 20.29 0.6042 0.9950 0.0050 0.3958 2.7798 0.6607 0.8247
KDE - Spatio-temporal change detection [10] 29.33 26.71 0.6576 0.9910 0.0090 0.3424 3.0022 0.6437 0.7341
GMM | RECTGAUSS-Tex [11] 34.83 32.57 0.5156 0.9862 0.0138 0.4844 3.6842 0.5221 0.7190
CDet [36] 3.50 5.00 0.9034 0.9917 0.0083 0.0966 1.1574 0.8608 0.8397
PAWCS [37] 4.00 2.00 0.8547 0.9949 0.0051 0.1453 1.1402 0.8579 0.8746
SuBSENSE [38] 7.00 5.00 0.8281 0.9938 0.0062 0.1719 1.5447 0.8260 0.8576
PBAS [14] 14.00 15.71 0.7840 0.9898 0.0102 0.2160 1.7693 0.7532 0.8160
Chebyshev prob. with Static Object detection [15] 21.33 21.86 0.7133 0.9888 0.0112 0.2867 2.3856 0.7001 0.7856
SC-SOBS [16] 21.17 20.86 0.8017 0.9831 0.0169 0.1983 2.4081 0.7283 0.7315
Bayesian Background [17] 31.17 33.00 0.6018 0.9826 0.0174 0.3982 3.3879 0.6272 0.7435
GMM | Stauffer & Grimson [18] 32.83 27.57 0.7108 0.9860 0.0140 0.2892 3.1037 0.6624 0.7012
KNN [19] 25.33 24.29 0.6707 0.9907 0.0093 0.3293 2.7954 0.6785 0.7882
SBBS [42] 13.83 19.00 0.7506 0.9859 0.0141 0.2494 2.1276 0.7678 0.8378
SGMM [21] 25.33 21.14 0.7073 0.9910 0.0090 0.2927 2.5311 0.7008 0.7812
pROST [39] 35.00 33.57 0.6735 0.9790 0.0210 0.3265 3.2534 0.6350 0.6734
Histogram [23] 36.83 34.71 0.7698 0.9343 0.0657 0.2302 6.9682 0.5485 0.5251
CDPS [24] 21.33 21.43 0.7769 0.9848 0.0152 0.2231 2.2747 0.7281 0.7610
GRBM [43] 22.17 15.14 0.8155 0.9879 0.0121 0.1845 1.8146 0.7748 0.7632
GRBM_without tuning [44] 25.50 26.43 0.7589 0.9811 0.0189 0.2411 2.7227 0.7162 0.7206
DPGMM [25] 15.33 14.71 0.8275 0.9855 0.0145 0.1725 2.1159 0.7763 0.7928
Spectral-360 [26] 10.17 12.14 0.7770 0.9920 0.0080 0.2230 1.8516 0.7770 0.8461
Multi-Layer Background Subtraction [27] 22.50 23.86 0.6936 0.9888 0.0112 0.3064 2.7658 0.6993 0.7960
SOBS_CF [34] 20.17 22.14 0.8211 0.9788 0.0212 0.1789 2.6466 0.7273 0.7139
SBM [45] 10.83 9.71 0.8063 0.9919 0.0081 0.1937 1.6820 0.8030 0.8252
SGMM-SOD [28] 11.00 12.00 0.7697 0.9938 0.0062 0.2303 1.4960 0.7661 0.8339
CwisarD [29] 16.17 19.71 0.8178 0.9781 0.0219 0.1822 2.6607 0.7780 0.7739
STBM [46] 8.00 8.29 0.8350 0.9911 0.0089 0.1650 1.6521 0.8157 0.8210
Multimode Background Subtraction Version 0 (MBS V0) [40] 10.83 7.57 0.7894 0.9939 0.0061 0.2106 1.4597 0.8092 0.8486
Multimode Background Subtraction(MBS) [41] 8.67 7.00 0.8103 0.9931 0.0069 0.1897 1.2808 0.8217 0.8480
GPRMF [31] 14.33 19.57 0.8372 0.9734 0.0266 0.1628 3.1583 0.7944 0.8144
TUBITAK UZAY 1 [32] 35.00 30.71 0.7794 0.9756 0.0244 0.2206 3.7014 0.6475 0.6237
PBAS-PID [33] 12.67 13.86 0.7967 0.9902 0.0098 0.2033 1.6904 0.7720 0.8162

Results, for the baseline category.

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Method Average ranking Average     Re  Average     Sp  Average   FPR  Average   FNR  Average   PWC     Average     F-Measure  Average   Precision 
SOBS [1] 11.57 0.9193 0.9980 0.0020 0.0807 0.4332 0.9251 0.9313
GMM | KaewTraKulPong [2] 24.86 0.5863 0.9987 0.0013 0.4137 1.9381 0.7119 0.9532
KDE - ElGammal [3] 20.00 0.8969 0.9977 0.0023 0.1031 0.5499 0.9092 0.9223
GMM | Zivkovic [4] 30.00 0.8085 0.9972 0.0028 0.1915 1.3298 0.8382 0.8993
Mahalanobis distance [5] 27.71 0.8872 0.9963 0.0037 0.1128 0.7290 0.8954 0.9071
Euclidean distance [6] 30.57 0.8385 0.9955 0.0045 0.1615 1.0260 0.8720 0.9114
Local-Self similarity [7] 27.29 0.9732 0.9865 0.0135 0.0268 1.3352 0.8494 0.7564
KDE - Integrated Spatio-temporal Features [8] 37.71 0.7472 0.9954 0.0046 0.2528 1.8058 0.7392 0.7998
PSP-MRF [9] 12.14 0.9319 0.9978 0.0022 0.0681 0.4127 0.9289 0.9261
RMoG (Region-based Mixture of Gaussians) [35] 27.00 0.7082 0.9981 0.0019 0.2918 1.5935 0.7848 0.9125
KDE - Spatio-temporal change detection [10] 38.29 0.7551 0.9940 0.0060 0.2449 1.9154 0.7554 0.7833
GMM | RECTGAUSS-Tex [11] 28.57 0.6669 0.9979 0.0021 0.3331 1.5342 0.7500 0.9175
CDet [36] 8.14 0.9704 0.9974 0.0026 0.0296 0.3589 0.9458 0.9238
PAWCS [37] 8.43 0.9408 0.9980 0.0020 0.0592 0.4491 0.9397 0.9394
SuBSENSE [38] 4.43 0.9520 0.9982 0.0018 0.0480 0.3574 0.9503 0.9495
PBAS [14] 18.29 0.9594 0.9970 0.0030 0.0406 0.4858 0.9242 0.8941
Chebyshev prob. with Static Object detection [15] 27.86 0.8266 0.9970 0.0030 0.1734 0.8304 0.8646 0.9143
SC-SOBS [16] 8.14 0.9327 0.9980 0.0020 0.0673 0.3747 0.9333 0.9341
Bayesian Background [17] 21.14 0.7327 0.9984 0.0016 0.2673 0.9037 0.8271 0.9620
GMM | Stauffer & Grimson [18] 35.57 0.8180 0.9948 0.0052 0.1820 1.5325 0.8245 0.8461
KNN [19] 24.57 0.7934 0.9979 0.0021 0.2066 1.2840 0.8411 0.9245
UBA [20] 33.43 0.9017 0.9912 0.0088 0.0983 1.0169 0.8132 0.7423
SGMM [21] 32.29 0.8680 0.9949 0.0051 0.1320 1.2436 0.8594 0.8584
Quasi-Continuous Histograms based Motion Detection [22] 41.43 0.7044 0.9923 0.0077 0.2956 2.2142 0.6616 0.7009
pROST [39] 34.43 0.8415 0.9937 0.0063 0.1585 1.1588 0.8289 0.8181
Histogram [23] 23.14 0.8777 0.9972 0.0028 0.1223 0.6679 0.9004 0.9254
CDPS [24] 21.14 0.9488 0.9965 0.0035 0.0512 0.6238 0.9208 0.8969
GRBM [43] 23.43 0.9108 0.9956 0.0044 0.0892 0.6674 0.9143 0.9210
GRBM_without tuning [44] 23.43 0.9108 0.9956 0.0044 0.0892 0.6674 0.9143 0.9210
DPGMM [25] 17.29 0.9632 0.9969 0.0031 0.0368 0.4949 0.9286 0.8984
Spectral-360 [26] 15.29 0.9615 0.9968 0.0032 0.0385 0.4263 0.9330 0.9066
Multi-Layer Background Subtraction [27] 16.71 0.8456 0.9984 0.0016 0.1544 0.8993 0.9004 0.9655
SOBS_CF [34] 11.00 0.9347 0.9978 0.0022 0.0653 0.3912 0.9299 0.9254
SBM [45] 15.86 0.9270 0.9973 0.0027 0.0730 0.4420 0.9250 0.9233
SGMM-SOD [28] 17.57 0.9334 0.9974 0.0026 0.0666 0.5494 0.9212 0.9113
CwisarD [29] 23.43 0.8989 0.9971 0.0029 0.1011 0.6630 0.9075 0.9171
STBM [46] 12.00 0.9524 0.9973 0.0027 0.0476 0.3840 0.9345 0.9188
Multimode Background Subtraction Version 0 (MBS V0) [40] 12.00 0.9158 0.9979 0.0021 0.0842 0.4361 0.9287 0.9431
Multimode Background Subtraction(MBS) [41] 12.00 0.9158 0.9979 0.0021 0.0842 0.4361 0.9287 0.9431
SBBS [42] 17.71 0.9417 0.9973 0.0027 0.0583 0.4947 0.9192 0.8994
GPRMF [31] 13.29 0.9060 0.9979 0.0021 0.0940 0.4669 0.9280 0.9524
TUBITAK UZAY 1 [32] 37.29 0.8936 0.9847 0.0153 0.1064 2.0851 0.7633 0.6767
PBAS-PID [33] 17.57 0.9576 0.9971 0.0029 0.0424 0.4862 0.9248 0.8968

Results, for the dynamic background category.

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Method Average ranking Average     Re  Average     Sp  Average   FPR  Average   FNR  Average   PWC     Average     F-Measure  Average   Precision 
SOBS [1] 28.57 0.8798 0.9843 0.0157 0.1202 1.6367 0.6439 0.5856
GMM | KaewTraKulPong [2] 23.71 0.6303 0.9983 0.0017 0.3697 0.5405 0.6697 0.7700
KDE - ElGammal [3] 33.00 0.8012 0.9856 0.0144 0.1988 1.6393 0.5961 0.5732
SuBSENSE [38] 10.71 0.7768 0.9994 0.0006 0.2232 0.4042 0.8177 0.8915
GMM | Zivkovic [4] 28.71 0.8019 0.9903 0.0097 0.1981 1.1725 0.6328 0.6213
Mahalanobis distance [5] 35.00 0.8132 0.9698 0.0302 0.1868 3.1407 0.5261 0.4517
Euclidean distance [6] 37.71 0.7757 0.9714 0.0286 0.2243 3.0095 0.5081 0.4487
Local-Self similarity [7] 33.57 0.8983 0.7694 0.2306 0.1017 22.7868 0.0949 0.0518
KDE - Integrated Spatio-temporal Features [8] 27.43 0.8401 0.9908 0.0092 0.1599 1.1501 0.6016 0.5413
PSP-MRF [9] 23.43 0.8955 0.9859 0.0141 0.1045 1.4514 0.6960 0.6576
RMoG (Region-based Mixture of Gaussians) [35] 19.00 0.7892 0.9978 0.0022 0.2108 0.4238 0.7352 0.7288
KDE - Spatio-temporal change detection [10] 23.00 0.8935 0.9908 0.0092 0.1065 1.0142 0.6574 0.5888
GMM | RECTGAUSS-Tex [11] 39.43 0.4776 0.9838 0.0162 0.5224 1.9735 0.4296 0.6478
Chebyshev probability approach [12] 13.71 0.8182 0.9982 0.0018 0.1818 0.3436 0.7656 0.7633
PAWCS [37] 5.86 0.8868 0.9989 0.0011 0.1132 0.1917 0.8938 0.9038
Color Histogram Backprojection [13] 42.86 0.6307 0.8906 0.1094 0.3693 11.0493 0.2675 0.1980
PBAS [14] 20.29 0.6955 0.9989 0.0011 0.3045 0.5394 0.6829 0.8326
Chebyshev prob. with Static Object detection [15] 16.00 0.8182 0.9976 0.0024 0.1818 0.4086 0.7520 0.7339
SC-SOBS [16] 26.86 0.8918 0.9836 0.0164 0.1082 1.6899 0.6686 0.6283
Bayesian Background [17] 34.14 0.5962 0.9917 0.0083 0.4038 1.2427 0.5369 0.6898
GMM | Stauffer & Grimson [18] 27.14 0.8344 0.9896 0.0104 0.1656 1.2083 0.6330 0.5989
KNN [19] 22.86 0.8047 0.9937 0.0063 0.1953 0.8059 0.6865 0.6931
SBBS [42] 10.14 0.7772 0.9994 0.0006 0.2228 0.2682 0.8128 0.9037
SGMM [21] 27.71 0.7715 0.9933 0.0067 0.2285 0.9132 0.6380 0.6665
Quasi-Continuous Histograms based Motion Detection [22] 26.14 0.8909 0.9896 0.0104 0.1091 1.1301 0.6430 0.5347
pROST [39] 30.00 0.7314 0.9952 0.0048 0.2686 0.6612 0.6180 0.5969
Histogram [23] 36.71 0.8069 0.9401 0.0599 0.1931 6.0488 0.2426 0.1516
CDPS [24] 24.00 0.7590 0.9947 0.0053 0.2410 0.7281 0.7495 0.8086
GRBM [43] 21.00 0.7011 0.9984 0.0016 0.2989 0.4164 0.7117 0.7463
GRBM_without tuning [44] 25.00 0.6287 0.9982 0.0018 0.3713 0.5427 0.6754 0.7436
DPGMM [25] 13.71 0.8852 0.9966 0.0034 0.1148 0.4121 0.8137 0.7762
STBM [46] 10.00 0.7805 0.9994 0.0006 0.2195 0.3087 0.8193 0.8732
Spectral-360 [26] 13.71 0.7748 0.9993 0.0007 0.2252 0.3464 0.7872 0.8590
Multi-Layer Background Subtraction [27] 31.29 0.7584 0.9912 0.0088 0.2416 1.0758 0.6278 0.6466
SOBS_CF [34] 26.71 0.9014 0.9820 0.0180 0.0986 1.8391 0.6519 0.5953
SBM [45] 18.43 0.7660 0.9982 0.0018 0.2340 0.4508 0.7882 0.8324
SGMM-SOD [28] 22.43 0.7786 0.9966 0.0034 0.2214 0.6041 0.6883 0.7044
CwisarD [29] 11.71 0.8355 0.9982 0.0018 0.1645 0.3389 0.8086 0.8096
DMB [30] 6.00 0.9155 0.9987 0.0013 0.0845 0.2282 0.8262 0.7877
Multimode Background Subtraction Version 0 (MBS V0) [40] 20.00 0.7637 0.9972 0.0028 0.2363 0.4848 0.7904 0.8606
Multimode Background Subtraction(MBS) [41] 19.00 0.7641 0.9972 0.0028 0.2359 0.4845 0.7915 0.8651
GPRMF [31] 20.43 0.8991 0.9877 0.0123 0.1009 1.2694 0.7726 0.7414
TUBITAK UZAY 1 [32] 26.57 0.8176 0.9920 0.0080 0.1824 1.0428 0.6078 0.5903
CDet [36] 2.57 0.9216 0.9992 0.0008 0.0784 0.1503 0.8991 0.8824
PBAS-PID [33] 18.71 0.7542 0.9989 0.0011 0.2458 0.4624 0.7357 0.8291

Results, for the camera jitter category.

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Method Average ranking Average     Re  Average     Sp  Average   FPR  Average   FNR  Average   PWC     Average     F-Measure  Average   Precision 
SOBS [1] 21.57 0.8007 0.9787 0.0213 0.1993 2.7479 0.7086 0.6399
GMM | KaewTraKulPong [2] 26.57 0.5074 0.9888 0.0112 0.4926 3.0233 0.5761 0.6897
KDE - ElGammal [3] 30.43 0.7375 0.9562 0.0438 0.2625 5.1349 0.5720 0.4862
SuBSENSE [38] 6.14 0.8243 0.9908 0.0092 0.1757 1.6469 0.8152 0.8115
GMM | Zivkovic [4] 33.86 0.6900 0.9665 0.0335 0.3100 4.4057 0.5670 0.4872
Mahalanobis distance [5] 34.00 0.7356 0.9431 0.0569 0.2644 6.4390 0.4960 0.3813
Euclidean distance [6] 35.86 0.7115 0.9456 0.0544 0.2885 6.2957 0.4874 0.3753
Local-Self similarity [7] 30.29 0.9764 0.6158 0.3842 0.0236 36.9570 0.2074 0.1202
KDE - Integrated Spatio-temporal Features [8] 21.00 0.7316 0.9857 0.0143 0.2684 2.4238 0.7110 0.6993
PSP-MRF [9] 14.71 0.8211 0.9825 0.0175 0.1789 2.2781 0.7502 0.7009
RMoG (Region-based Mixture of Gaussians) [35] 24.14 0.6669 0.9864 0.0136 0.3331 2.6794 0.7010 0.7605
KDE - Spatio-temporal change detection [10] 21.43 0.7562 0.9816 0.0184 0.2438 2.7450 0.7122 0.6793
GMM | RECTGAUSS-Tex [11] 30.71 0.7649 0.9497 0.0503 0.2351 5.6663 0.5370 0.4179
CDet [36] 12.00 0.8962 0.9816 0.0184 0.1038 2.1987 0.8180 0.7689
PAWCS [37] 5.71 0.7840 0.9935 0.0065 0.2160 1.4220 0.8137 0.8660
Color Histogram Backprojection [13] 33.14 0.4688 0.9821 0.0179 0.5312 3.7175 0.4822 0.5296
PBAS [14] 19.43 0.7373 0.9838 0.0162 0.2627 2.4882 0.7220 0.7586
Chebyshev prob. with Static Object detection [15] 28.86 0.7223 0.9725 0.0275 0.2777 3.6203 0.6416 0.5960
SC-SOBS [16] 22.43 0.8113 0.9768 0.0232 0.1887 2.8794 0.7051 0.6286
Bayesian Background [17] 26.57 0.5441 0.9886 0.0114 0.4559 2.8807 0.5988 0.6678
GMM | Stauffer & Grimson [18] 29.29 0.7334 0.9666 0.0334 0.2666 4.2269 0.5969 0.5126
KNN [19] 24.71 0.7351 0.9778 0.0222 0.2649 3.1104 0.6894 0.7018
SBBS [42] 15.57 0.7322 0.9874 0.0126 0.2678 2.1608 0.7347 0.7950
SGMM [21] 19.71 0.7088 0.9869 0.0131 0.2912 2.3761 0.7251 0.7752
pROST [39] 13.57 0.7692 0.9872 0.0128 0.2308 2.0370 0.7478 0.7338
Histogram [23] 37.86 0.7111 0.8412 0.1588 0.2889 16.2797 0.2784 0.1756
CDPS [24] 36.71 0.6025 0.9613 0.0387 0.3975 5.3593 0.4865 0.4397
GRBM [43] 27.00 0.7154 0.9788 0.0212 0.2846 3.1889 0.6555 0.6717
GRBM_without tuning [44] 26.57 0.8134 0.9594 0.0406 0.1866 4.6494 0.6126 0.5014
DPGMM [25] 13.43 0.6988 0.9930 0.0070 0.3012 1.7707 0.7477 0.8426
Spectral-360 [26] 17.57 0.6709 0.9906 0.0094 0.3291 2.0806 0.7156 0.8392
Multi-Layer Background Subtraction [27] 17.71 0.6903 0.9905 0.0095 0.3097 2.1628 0.7311 0.7905
SOBS_CF [34] 20.57 0.8218 0.9768 0.0232 0.1782 2.8437 0.7150 0.6405
SBM [45] 18.29 0.7072 0.9886 0.0114 0.2928 2.3277 0.7413 0.7871
SGMM-SOD [28] 20.29 0.6113 0.9907 0.0093 0.3887 2.3608 0.6724 0.8040
CwisarD [29] 9.43 0.7645 0.9916 0.0084 0.2355 1.7886 0.7814 0.8091
STBM [46] 16.29 0.7641 0.9852 0.0148 0.2359 2.3825 0.7522 0.7616
Multimode Background Subtraction Version 0 (MBS V0) [40] 3.43 0.8321 0.9929 0.0071 0.1679 1.5408 0.8367 0.8443
Multimode Background Subtraction(MBS) [41] 3.43 0.8321 0.9929 0.0071 0.1679 1.5408 0.8367 0.8443
GPRMF [31] 3.29 0.8159 0.9953 0.0047 0.1841 1.1062 0.8596 0.9244
TUBITAK UZAY 1 [32] 27.71 0.8646 0.9439 0.0561 0.1354 5.8825 0.5661 0.4247
PBAS-PID [33] 20.71 0.7210 0.9853 0.0147 0.2790 2.4659 0.7206 0.7586

Results, for the intermittent object motion category.

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Method Average ranking Average     Re  Average     Sp  Average   FPR  Average   FNR  Average   PWC     Average     F-Measure  Average   Precision 
SOBS [1] 25.57 0.7057 0.9507 0.0493 0.2943 6.1324 0.5628 0.5531
GMM | KaewTraKulPong [2] 26.86 0.3476 0.9892 0.0108 0.6524 5.9854 0.3903 0.6953
KDE - ElGammal [3] 36.71 0.5035 0.9309 0.0691 0.4965 10.0695 0.4088 0.4609
GMM | Zivkovic [4] 26.14 0.5467 0.9712 0.0288 0.4533 5.4986 0.5325 0.6458
Mahalanobis distance [5] 32.43 0.7165 0.8886 0.1114 0.2835 11.5341 0.4968 0.4535
Euclidean distance [6] 33.00 0.5919 0.9336 0.0664 0.4081 8.9975 0.4892 0.4995
Local-Self similarity [7] 28.57 0.9027 0.8222 0.1778 0.0973 15.8827 0.5329 0.4445
KDE - Integrated Spatio-temporal Features [8] 16.71 0.4512 0.9964 0.0036 0.5488 4.4191 0.5454 0.8166
PSP-MRF [9] 25.14 0.7010 0.9530 0.0470 0.2990 6.0594 0.5645 0.5727
RMoG (Region-based Mixture of Gaussians) [35] 17.86 0.4488 0.9950 0.0050 0.5512 4.6882 0.5431 0.8026
KDE - Spatio-temporal change detection [10] 22.00 0.4372 0.9923 0.0077 0.5628 4.6997 0.5039 0.7212
GMM | RECTGAUSS-Tex [11] 25.86 0.2190 0.9977 0.0023 0.7810 5.2547 0.3146 0.5850
CDet [36] 5.14 0.8865 0.9891 0.0109 0.1135 1.6116 0.8039 0.7821
PAWCS [37] 4.71 0.7487 0.9945 0.0055 0.2513 2.3536 0.7764 0.8392
SuBSENSE [38] 12.57 0.6578 0.9915 0.0085 0.3422 3.8349 0.6569 0.7957
PBAS [14] 20.14 0.6700 0.9751 0.0249 0.3300 4.2871 0.5745 0.7045
Chebyshev prob. with Static Object detection [15] 29.14 0.3570 0.9807 0.0193 0.6430 6.4700 0.3863 0.7688
SC-SOBS [16] 20.57 0.7237 0.9613 0.0387 0.2763 5.2207 0.5918 0.5896
Bayesian Background [17] 37.43 0.4813 0.9304 0.0696 0.5187 9.9632 0.4081 0.4747
GMM | Stauffer & Grimson [18] 23.71 0.5142 0.9835 0.0165 0.4858 5.1955 0.5207 0.6688
KNN [19] 23.29 0.4617 0.9865 0.0135 0.5383 5.1370 0.5026 0.7121
UBA [20] 13.00 0.7205 0.9827 0.0173 0.2795 3.0544 0.6886 0.7310
SGMM [21] 22.29 0.5013 0.9853 0.0147 0.4987 4.9180 0.5397 0.6993
Quasi-Continuous Histograms based Motion Detection [22] 31.00 0.4407 0.9797 0.0203 0.5593 6.0490 0.4367 0.5384
pROST [39] 35.00 0.5156 0.9317 0.0683 0.4844 8.5201 0.4127 0.4740
Histogram [23] 29.86 0.7512 0.8656 0.1344 0.2488 13.1359 0.5112 0.4859
CDPS [24] 10.86 0.8084 0.9765 0.0235 0.1916 3.4650 0.7406 0.7624
GRBM [43] 5.57 0.8467 0.9875 0.0125 0.1533 2.4804 0.8115 0.8023
GRBM_without tuning [44] 22.00 0.7466 0.9540 0.0460 0.2534 6.7084 0.5987 0.5623
DPGMM [25] 26.71 0.6763 0.9470 0.0530 0.3237 6.8457 0.5418 0.6525
Spectral-360 [26] 21.00 0.5945 0.9811 0.0189 0.4055 5.4443 0.5656 0.7192
Multi-Layer Background Subtraction [27] 31.14 0.5012 0.9629 0.0371 0.4988 7.0245 0.4816 0.6024
SOBS_CF [34] 23.14 0.7641 0.9381 0.0619 0.2359 6.7454 0.5810 0.5464
SBM [45] 14.00 0.7068 0.9850 0.0150 0.2932 4.1545 0.6755 0.7352
SGMM-SOD [28] 7.86 0.7363 0.9909 0.0091 0.2637 2.5238 0.7151 0.8141
CwisarD [29] 25.71 0.7847 0.9003 0.0997 0.2153 10.0314 0.5674 0.5013
STBM [46] 14.00 0.7166 0.9837 0.0163 0.2834 4.1910 0.6780 0.7220
Multimode Background Subtraction Version 0 (MBS V0) [40] 10.57 0.6386 0.9931 0.0069 0.3614 3.1858 0.7092 0.8201
Multimode Background Subtraction(MBS) [41] 8.86 0.7418 0.9862 0.0138 0.2582 2.3008 0.7568 0.7827
SBBS [42] 19.86 0.7616 0.9399 0.0601 0.2384 6.3180 0.6795 0.6772
GPRMF [31] 33.71 0.6101 0.8753 0.1247 0.3899 13.0733 0.4870 0.5863
TUBITAK UZAY 1 [32] 28.43 0.5823 0.9645 0.0355 0.4177 6.3265 0.5091 0.5533
PBAS-PID [33] 17.86 0.7048 0.9759 0.0241 0.2952 3.9085 0.6267 0.7055

Results, for the shadow category.

Click on method name for more details.

Method Average ranking Average     Re  Average     Sp  Average   FPR  Average   FNR  Average   PWC     Average     F-Measure  Average   Precision  Average   FPR-S 
SOBS [1] 30.43 0.8350 0.9836 0.0164 0.1650 2.3366 0.7716 0.7219 0.5689
GMM | KaewTraKulPong [2] 23.00 0.6323 0.9936 0.0064 0.3677 2.3015 0.7176 0.8577 0.4069
KDE - ElGammal [3] 23.86 0.8536 0.9885 0.0115 0.1464 1.6881 0.8028 0.7660 0.6217
GMM | Zivkovic [4] 31.00 0.7770 0.9878 0.0122 0.2230 2.1957 0.7319 0.7232 0.5428
Mahalanobis distance [5] 39.00 0.7845 0.9708 0.0292 0.2155 3.7896 0.6348 0.5685 0.5899
Euclidean distance [6] 36.14 0.8001 0.9783 0.0217 0.1999 2.8987 0.6785 0.6112 0.5763
Local-Self similarity [7] 31.71 0.9584 0.9442 0.0558 0.0416 5.5496 0.5951 0.4673 0.6377
KDE - Integrated Spatio-temporal Features [8] 20.57 0.7197 0.9930 0.0070 0.2803 2.1292 0.7545 0.8244 0.3901
PSP-MRF [9] 26.00 0.8736 0.9829 0.0171 0.1264 2.2414 0.7907 0.7281 0.5861
RMoG (Region-based Mixture of Gaussians) [35] 23.71 0.6680 0.9936 0.0064 0.3320 2.1720 0.7212 0.8073 0.3097
KDE - Spatio-temporal change detection [10] 31.00 0.6970 0.9898 0.0102 0.3030 2.4865 0.7136 0.7559 0.3977
GMM | RECTGAUSS-Tex [11] 31.29 0.7189 0.9886 0.0114 0.2811 2.4111 0.7331 0.7840 0.4764
Chebyshev probability approach [12] 18.14 0.8669 0.9887 0.0113 0.1331 1.5552 0.8333 0.8104 0.4204
PAWCS [37] 4.29 0.9172 0.9932 0.0068 0.0828 1.0230 0.8913 0.8710 0.4815
SuBSENSE [38] 3.71 0.9419 0.9920 0.0080 0.0581 1.0120 0.8986 0.8646 0.5996
PBAS [14] 11.57 0.9133 0.9904 0.0096 0.0867 1.2753 0.8597 0.8143 0.5789
Chebyshev prob. with Static Object detection [15] 18.86 0.8670 0.9887 0.0113 0.1330 1.5561 0.8333 0.8103 0.4204
SC-SOBS [16] 29.57 0.8502 0.9834 0.0166 0.1498 2.3000 0.7786 0.7230 0.6035
Bayesian Background [17] 29.29 0.6537 0.9916 0.0084 0.3463 2.4695 0.6955 0.7791 0.3293
GMM | Stauffer & Grimson [18] 30.43 0.7960 0.9871 0.0129 0.2040 2.1951 0.7370 0.7156 0.5352
KNN [19] 23.71 0.7478 0.9916 0.0084 0.2522 2.0569 0.7468 0.7788 0.3979
UBA [20] 32.00 0.9084 0.9707 0.0293 0.0916 3.2250 0.7123 0.6095 0.6147
SGMM [21] 22.57 0.8580 0.9889 0.0111 0.1420 1.7965 0.7944 0.7617 0.4865
Quasi-Continuous Histograms based Motion Detection [22] 34.00 0.6949 0.9887 0.0113 0.3051 2.5870 0.7072 0.7378 0.3483
pROST [39] 37.71 0.7541 0.9791 0.0209 0.2459 2.9907 0.6765 0.6239 0.5167
Histogram [23] 38.00 0.8308 0.9686 0.0314 0.1692 3.7098 0.6589 0.6009 0.5881
CDPS [24] 20.57 0.9233 0.9846 0.0154 0.0767 1.9516 0.8092 0.7567 0.5902
GRBM [43] 22.71 0.8725 0.9857 0.0143 0.1275 1.7683 0.8046 0.7559 0.5946
GRBM_without tuning [44] 27.00 0.7427 0.9902 0.0098 0.2573 1.9146 0.7405 0.7553 0.4654
DPGMM [25] 15.71 0.8545 0.9916 0.0084 0.1455 1.5947 0.8127 0.8240 0.4179
Spectral-360 [26] 7.57 0.9366 0.9905 0.0095 0.0634 1.1784 0.8843 0.8412 0.6213
Multi-Layer Background Subtraction [27] 16.43 0.8588 0.9912 0.0088 0.1412 1.5621 0.8216 0.8099 0.4879
SOBS_CF [34] 28.14 0.8699 0.9828 0.0172 0.1301 2.2579 0.7721 0.7045 0.5899
SBM [45] 13.14 0.8629 0.9910 0.0090 0.1371 1.3780 0.8458 0.8459 0.4363
SGMM-SOD [28] 10.43 0.9191 0.9902 0.0098 0.0809 1.2534 0.8646 0.8226 0.6343
CwisarD [29] 15.57 0.8872 0.9897 0.0103 0.1128 1.3757 0.8412 0.8056 0.5552
STBM [46] 13.86 0.8979 0.9896 0.0104 0.1021 1.3643 0.8529 0.8221 0.5742
Multimode Background Subtraction Version 0 (MBS V0) [40] 20.43 0.7762 0.9918 0.0082 0.2238 1.5794 0.7784 0.8063 0.3481
Multimode Background Subtraction(MBS) [41] 15.86 0.7920 0.9924 0.0076 0.2080 1.4940 0.7968 0.8262 0.3481
SBBS [42] 21.29 0.5981 0.9970 0.0030 0.4019 1.8693 0.7105 0.8934 0.1228
GPRMF [31] 4.71 0.9253 0.9922 0.0078 0.0747 1.0712 0.8889 0.8671 0.4932
TUBITAK UZAY 1 [32] 31.86 0.8594 0.9768 0.0232 0.1406 2.8893 0.7509 0.6843 0.5651
CDet [36] 12.29 0.9259 0.9892 0.0108 0.0741 1.4108 0.8644 0.8122 0.6611
PBAS-PID [33] 10.86 0.9115 0.9907 0.0093 0.0885 1.2606 0.8617 0.8193 0.5747

Results, for the thermal category.

Click on method name for more details.

Method Average ranking Average     Re  Average     Sp  Average   FPR  Average   FNR  Average   PWC     Average     F-Measure  Average   Precision 
SOBS [1] 24.29 0.5888 0.9956 0.0044 0.4112 2.0983 0.6834 0.8754
GMM | KaewTraKulPong [2] 24.29 0.3395 0.9993 0.0007 0.6605 4.8419 0.4767 0.9709
KDE - ElGammal [3] 17.00 0.6725 0.9955 0.0045 0.3275 1.6795 0.7423 0.8974
GMM | Zivkovic [4] 28.86 0.5542 0.9942 0.0058 0.4458 4.3002 0.6548 0.8706
Mahalanobis distance [5] 29.71 0.6270 0.9906 0.0094 0.3730 2.3462 0.7065 0.8617
Euclidean distance [6] 33.00 0.5111 0.9907 0.0093 0.4889 3.8516 0.6313 0.8877
Local-Self similarity [7] 26.14 0.9036 0.9692 0.0308 0.0964 3.2612 0.7297 0.6433
KDE - Integrated Spatio-temporal Features [8] 26.14 0.4147 0.9981 0.0019 0.5853 5.4152 0.4989 0.9164
PSP-MRF [9] 19.86 0.5991 0.9962 0.0038 0.4009 1.9189 0.6932 0.9218
RMoG (Region-based Mixture of Gaussians) [35] 24.86 0.3441 0.9991 0.0009 0.6559 5.1222 0.4788 0.9365
KDE - Spatio-temporal change detection [10] 28.00 0.4065 0.9973 0.0027 0.5935 5.1527 0.5199 0.8761
GMM | RECTGAUSS-Tex [11] 25.57 0.2461 0.9994 0.0006 0.7539 5.2656 0.3682 0.9619
Chebyshev probability approach [12] 14.00 0.6940 0.9962 0.0038 0.3060 1.3285 0.7259 0.8910
PAWCS [37] 17.57 0.8504 0.9910 0.0090 0.1496 1.4018 0.8324 0.8280
SuBSENSE [38] 22.00 0.8161 0.9908 0.0092 0.1839 2.0125 0.8171 0.8328
PBAS [14] 17.00 0.7283 0.9934 0.0066 0.2717 1.5398 0.7556 0.8922
Chebyshev prob. with Static Object detection [15] 14.71 0.6887 0.9963 0.0037 0.3113 1.4283 0.7230 0.8906
SC-SOBS [16] 22.43 0.6003 0.9957 0.0043 0.3997 1.9841 0.6923 0.8857
Bayesian Background [17] 23.43 0.6026 0.9952 0.0048 0.3974 2.8676 0.6969 0.8877
GMM | Stauffer & Grimson [18] 28.14 0.5691 0.9946 0.0054 0.4309 4.2642 0.6621 0.8652
KNN [19] 25.29 0.4817 0.9970 0.0030 0.5183 4.3783 0.6046 0.9186
UBA [20] 23.29 0.6880 0.9939 0.0061 0.3120 1.6684 0.7283 0.7962
SGMM [21] 22.71 0.5363 0.9970 0.0030 0.4637 3.9394 0.6481 0.9263
Quasi-Continuous Histograms based Motion Detection [22] 28.57 0.3350 0.9982 0.0018 0.6650 5.1493 0.4651 0.8784
pROST [39] 39.14 0.4290 0.9872 0.0128 0.5710 4.1526 0.5260 0.7936
Histogram [23] 26.86 0.6412 0.9933 0.0067 0.3588 1.9669 0.6996 0.8110
CDPS [24] 20.43 0.6195 0.9950 0.0050 0.3805 1.5205 0.6619 0.9014
GRBM [43] 26.57 0.8464 0.9812 0.0188 0.1536 2.3663 0.7511 0.6818
GRBM_without tuning [44] 24.86 0.7114 0.9893 0.0107 0.2886 1.8537 0.7558 0.8397
DPGMM [25] 21.43 0.8869 0.9882 0.0118 0.1131 1.5773 0.8134 0.7629
Spectral-360 [26] 16.14 0.7238 0.9939 0.0061 0.2762 1.6337 0.7764 0.9114
Multi-Layer Background Subtraction [27] 21.43 0.5072 0.9986 0.0014 0.4928 3.8704 0.6331 0.9611
SOBS_CF [34] 21.29 0.6347 0.9953 0.0047 0.3653 1.8021 0.7140 0.8715
SBM [45] 16.14 0.8677 0.9914 0.0086 0.1323 1.3390 0.8423 0.8275
SGMM-SOD [28] 14.71 0.6396 0.9971 0.0029 0.3604 1.6846 0.7353 0.9471
CwisarD [29] 22.14 0.7357 0.9918 0.0082 0.2643 1.7664 0.7619 0.8007
STBM [46] 15.14 0.8986 0.9913 0.0087 0.1014 1.2822 0.8571 0.8280
Multimode Background Subtraction Version 0 (MBS V0) [40] 21.29 0.8101 0.9908 0.0092 0.1899 1.5315 0.8115 0.8174
Multimode Background Subtraction(MBS) [41] 17.57 0.8162 0.9920 0.0080 0.1838 1.4289 0.8194 0.8268
SBBS [42] 19.71 0.6929 0.9941 0.0059 0.3071 1.6545 0.7499 0.8579
GPRMF [31] 19.57 0.8666 0.9917 0.0083 0.1334 1.9628 0.8305 0.8145
TUBITAK UZAY 1 [32] 29.14 0.6589 0.9920 0.0080 0.3411 3.9821 0.6877 0.8127
CDet [36] 13.00 0.8200 0.9940 0.0060 0.1800 1.2142 0.8337 0.8686
PBAS-PID [33] 16.57 0.7308 0.9936 0.0064 0.2692 1.5590 0.7622 0.8881

Results for methods [1, 3, 5, 21] have been obtained by the organizing committee using authors' original code. Results for methods [2, 4, 6, 7] have been obtained by the organizing committee using their own implementation or OpenCV. A 5x5 median filter has been applied in a post-processing step.

Metrics:

  • Average ranking across categories : (rank:Baseline + rank:Dynamic Background + rank:Camera Jitter + rank:Intermittent Object Motion + rank:Shadow + rank:Thermal) / 6
  • Average ranking : (rank:Recall + rank:Spec + rank:FPR + rank:FNR + rank:PWC + rank:FMeasure + rank:Precision) / 7
  • TP : True Positive
  • FP : False Positive
  • FN : False Negative
  • TN : True Negative
  • Re (Recall) : TP / (TP + FN)
  • Sp (Specificity) : TN / (TN + FP)
  • FPR (False Positive Rate) : FP / (FP + TN)
  • FNR (False Negative Rate) : FN / (TP + FN)
  • PWC (Percentage of Wrong Classifications) : 100 * (FN + FP) / (TP + FN + FP + TN)
  • F-Measure : (2 * Precision * Recall) / (Precision + Recall)
  • Precision : TP / (TP + FP)
  • FPR-S : Average False positive rate in hard shadow areas


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