Contact nameN/A N/A
Contact email[email protected]
Contact university/companyAnonymous
Method's nameGRASTA
ReferenceJun He et al. Incremental Gradient on the Grassmannian for Online Foreground and Background Separation in Subsampled Video, CVPR 2012
Processing timeN/A
Code is available onlineTrue
Web pagehttps://sites.google.com/site/hejunzz/grasta
Parameters rank=5, maxCycle = 10; OPTIONS.QUIET = 1; OPTIONS.MAX_LEVEL = 20;

You can download the background estimation results of this method by clicking here.

Video AGE pEPs pCEPS MSSSIM PSNR CQM
skating 25.9610 0.4342 0.3735 0.9042 17.7502 19.5429
wetSnow 37.7272 0.9296 0.8706 0.9148 16.2142 17.6186
I_SI_01 3.3333 0.0017 0.0003 0.9948 34.9123 35.5368
streetCornerAtNight 10.1120 0.1096 0.0658 0.9298 25.8923 27.4449
Hybrid 6.5101 0.0259 0.0006 0.9798 29.5586 30.1112
511 6.1220 0.0590 0.0032 0.9543 27.9466 29.8288
Blurred 47.3112 0.8652 0.7613 0.6873 13.8911 14.9294
CamouflageFgObjects 5.0457 0.0136 0.0001 0.9870 31.3085 31.8332
IntelligentRoom 3.4871 0.0068 0.0004 0.9908 33.6864 34.1154
PETS2006 5.5686 0.0560 0.0447 0.9706 29.0976 29.8069
IPPR2 6.9256 0.0179 0.0047 0.9484 29.1580 29.4534
MPEG4_40 5.6052 0.0321 0.0059 0.9551 29.4124 30.3959
ComplexBackground 6.2202 0.0362 0.0015 0.9854 28.8392 29.5619
Intersection 13.9690 0.0634 0.0014 0.9837 24.7790 25.9724
fluidHighway 9.3360 0.0822 0.0600 0.8917 26.2426 25.9185
highway 4.1048 0.0212 0.0012 0.9838 30.3754 31.4116
Video AGE pEPs pCEPS MSSSIM PSNR CQM
overpass 14.7489 0.1749 0.0610 0.7173 19.2118 20.2226
advertisementBoard 3.4812 0.0105 0.0068 0.9856 30.3567 31.2039
canoe 14.9438 0.2606 0.0412 0.6779 21.0638 21.9174
fountain01 5.7539 0.0548 0.0115 0.9388 26.4700 27.6413
fountain02 7.0811 0.0557 0.0034 0.9332 27.7658 28.6797
fall 24.6026 0.3652 0.1349 0.7428 16.3522 17.4530
Video AGE pEPs pCEPS MSSSIM PSNR CQM
O_SM_04 12.0262 0.1144 0.0044 0.9542 24.7051 25.9687
boulevard 15.4555 0.1611 0.0280 0.9227 21.8657 23.3684
I_SM_04 2.5355 0.0129 0.0001 0.9932 33.4652 34.3155
I_MC_02 13.9334 0.1716 0.0547 0.7824 19.6070 20.8916
badminton 17.1787 0.2236 0.1356 0.6856 21.0128 22.0785
O_MC_02 17.3914 0.2436 0.1160 0.6545 18.8690 19.7473
traffic 24.5624 0.6543 0.5900 0.8534 19.0189 20.1537
CMU 6.9292 0.0688 0.0028 0.9836 26.5720 27.4346
sidewalk 24.1964 0.3163 0.1851 0.3987 15.7239 17.5052
Video AGE pEPs pCEPS MSSSIM PSNR CQM
Uturn 23.5190 0.5863 0.3542 0.9013 18.8099 19.9950
CaVignal 1.7240 0.0005 0.0000 0.9961 39.3623 39.7338
office 9.1716 0.0225 0.0018 0.9804 27.3305 28.5189
sofa 4.2711 0.0455 0.0334 0.9362 26.8615 27.9841
copyMachine 8.2640 0.0578 0.0403 0.9186 22.0493 23.6524
tramstop 2.4268 0.0059 0.0006 0.9932 34.5318 35.0331
UCF-traffic 33.0449 0.9594 0.8991 0.9301 17.5132 20.0465
streetCorner 7.7734 0.0550 0.0157 0.9494 25.5233 26.6261
AVSS2007 21.3776 0.1765 0.1444 0.7610 15.2109 16.4993
Teknomo 6.7382 0.0479 0.0121 0.9744 25.7054 26.7183
I_CA_02 6.6146 0.0763 0.0531 0.9004 25.0824 25.7118
Candela_m1.10 3.8889 0.0179 0.0078 0.9676 28.5397 29.2630
I_MB_02 8.6360 0.0636 0.0486 0.8763 20.5592 21.7037
I_MB_01 7.3860 0.0546 0.0414 0.9624 26.6667 27.5051
busStation 3.5409 0.0051 0.0024 0.9763 33.0070 33.7178
I_CA_01 15.4496 0.3103 0.2533 0.9675 22.9758 23.7197
Video AGE pEPs pCEPS MSSSIM PSNR CQM
People&Foliage 46.3803 0.5511 0.4603 0.4193 11.1695 12.2090
boulevardJam 26.4716 0.3674 0.2249 0.5787 16.8102 18.1346
Crowded 6.4445 0.0331 0.0194 0.9587 29.1223 30.2332
tramway 10.3969 0.1007 0.0208 0.9221 22.7015 24.2460
groupCampus 18.6703 0.3348 0.2049 0.6168 19.5391 20.4323
HumanBody2 5.3034 0.0226 0.0029 0.9817 27.8108 28.8351
Board 28.1608 0.5251 0.3076 -0.2798 16.9619 18.1368
Foliage 12.4244 0.1374 0.0518 0.9656 22.0162 22.6277
UCF-fishes 10.5917 0.0989 0.0714 0.7065 22.9093 25.3412
ICRA3 15.6747 0.2564 0.1858 0.9005 21.6300 22.6644
IndianTraffic3 2.7481 0.0104 0.0026 0.9790 33.6611 34.8875
Video AGE pEPs pCEPS MSSSIM PSNR CQM
I_IL_01 23.6585 0.4885 0.4296 0.9061 19.2776 20.3746
I_IL_02 7.5423 0.0798 0.0515 0.9573 25.4671 26.2078
Dataset3Camera1 22.0816 0.5083 0.3689 0.9163 19.0471 20.2745
CameraParameter 6.1471 0.0062 0.0012 0.9811 31.0165 32.2542
Dataset3Camera2 5.7156 0.0523 0.0105 0.9845 27.8132 28.8821
cubicle 19.4842 0.4465 0.3281 0.9291 20.0825 21.2087
Video AGE pEPs pCEPS MSSSIM PSNR CQM
PedAndStorrowDrive3 3.6693 0.0192 0.0087 0.9895 29.9828 31.0679
PedAndStorrowDrive 5.0913 0.0302 0.0039 0.9836 28.9621 30.1087
BusStopMorning 5.7055 0.0307 0.0008 0.9868 28.9892 29.7766
Dataset4Camera1 3.1886 0.0122 0.0058 0.9829 30.7531 30.9434
Terrace 18.9514 0.2698 0.1151 0.8123 19.5965 20.7802
Video AGE pEPs pCEPS MSSSIM PSNR CQM
Toscana 6.4773 0.0488 0.0288 0.9031 22.8488 23.7504
CUHK_Square 4.8949 0.0220 0.0006 0.9736 30.5415 30.9483
pedestrians 4.9441 0.0035 0.0000 0.9908 31.6270 31.9282
NoisyNight 5.5040 0.0197 0.0020 0.9241 30.4161 31.3189
peopleInShade 6.5455 0.0013 0.0000 0.9879 30.5537 31.3878
DynamicBackground 7.8492 0.0607 0.0015 0.9636 27.4898 28.1986
snowFall 31.0542 0.7570 0.7260 0.8869 17.4874 18.6814
TwoLeaveShop1cor 4.0172 0.0248 0.0153 0.9447 28.8172 29.4332
MIT 5.7849 0.0465 0.0041 0.9645 28.1495 29.3101
TownCentre 4.4247 0.0103 0.0006 0.9699 32.0856 32.8171