Contact nameMaurizio Giordano
Contact email[email protected]
Contact university/companyICAR - Consiglio Nazionale delle Ricerche
Method's nameBEWiS
ReferenceMassimo De Gregorio and Maurizio Giordano, "Background estimation by weightless neural networks” - submitted to Pattern Recognition Letters
Processing time 3 fps for a 360x240 video with OpenMP parallelized C++ code on a QuadCore i7
Code is available onlineTrue
Web pagehttps://github.com/giordamaug/BEWiS
Parameters 4-bit neuron (b=4), 256 color scaling in RGB space (n=256,m=RGB), unity reward&punish (p=1:1), saturation value = 50 (u=50), always train pixels (k=-1).

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

Video AGE pEPs pCEPS MSSSIM PSNR CQM
skating 5.3344 0.0276 0.0074 0.9580 22.6638 24.3698
wetSnow 3.4244 0.0214 0.0145 0.9731 30.3484 31.1961
I_SI_01 2.3405 0.0017 0.0002 0.9948 37.8852 38.2852
streetCornerAtNight 2.7893 0.0008 0.0002 0.9833 36.5684 37.5805
Hybrid 4.1145 0.0058 0.0000 0.9898 32.9509 33.4677
511 3.5316 0.0242 0.0004 0.9808 31.2115 33.0870
Blurred 1.5186 0.0000 0.0000 0.9976 41.0599 41.2976
CamouflageFgObjects 3.7060 0.0204 0.0125 0.9797 30.1674 30.8818
IntelligentRoom 3.1865 0.0018 0.0000 0.9924 35.0876 35.4253
PETS2006 1.8269 0.0026 0.0017 0.9899 34.7080 35.4148
IPPR2 6.9032 0.0254 0.0122 0.9244 26.9198 27.5476
MPEG4_40 1.7021 0.0083 0.0006 0.9914 36.1269 37.1546
ComplexBackground 6.4103 0.0318 0.0015 0.9854 28.8501 29.6253
Intersection 2.5135 0.0022 0.0000 0.9860 36.6546 37.1458
fluidHighway 8.4373 0.0427 0.0315 0.9505 27.2190 27.2005
highway 7.3382 0.0419 0.0098 0.9552 27.3016 28.3772
Video AGE pEPs pCEPS MSSSIM PSNR CQM
overpass 6.4600 0.0755 0.0052 0.9097 26.6900 27.7637
advertisementBoard 1.9999 0.0035 0.0017 0.9938 36.4428 37.0137
canoe 14.6321 0.2497 0.0455 0.6651 20.5574 21.1235
fountain01 5.5050 0.0524 0.0102 0.9378 26.3198 27.5050
fountain02 5.1842 0.0240 0.0011 0.9608 30.4422 31.3007
fall 24.2842 0.3494 0.1080 0.7189 15.9998 17.1041
Video AGE pEPs pCEPS MSSSIM PSNR CQM
O_SM_04 5.9091 0.0391 0.0003 0.9667 29.2007 30.2477
boulevard 10.3209 0.1344 0.0198 0.8891 21.3146 22.8651
I_SM_04 3.7473 0.0333 0.0007 0.9849 29.8591 30.8215
I_MC_02 15.9705 0.1999 0.0874 0.7147 18.4206 19.6247
badminton 2.4827 0.0145 0.0114 0.9641 28.1868 29.3508
O_MC_02 11.6324 0.1536 0.0464 0.7943 21.4860 22.3386
traffic 6.5829 0.0460 0.0221 0.9162 28.8186 29.5370
CMU 4.5779 0.0250 0.0051 0.9870 29.7680 30.5310
sidewalk 23.5169 0.2972 0.1689 0.4545 15.6125 17.4051
Video AGE pEPs pCEPS MSSSIM PSNR CQM
Uturn 2.8864 0.0078 0.0031 0.9829 31.9702 32.6505
CaVignal 1.9820 0.0081 0.0036 0.9785 30.4862 31.5371
office 7.8138 0.0361 0.0257 0.9553 24.7658 25.6503
sofa 3.5433 0.0264 0.0174 0.9589 26.9413 27.9533
copyMachine 3.1396 0.0061 0.0007 0.9848 34.5166 35.2668
tramstop 4.2390 0.0227 0.0002 0.9913 31.5152 32.1432
UCF-traffic 1.7550 0.0105 0.0058 0.9735 33.1355 35.4670
streetCorner 9.7051 0.0854 0.0552 0.8675 20.7801 21.9184
AVSS2007 17.0903 0.1382 0.1128 0.7784 17.0107 18.0036
Teknomo 6.7955 0.0474 0.0180 0.9506 26.1940 27.2137
I_CA_02 2.6586 0.0038 0.0001 0.9949 36.2705 36.8607
Candela_m1.10 3.1395 0.0130 0.0078 0.9698 29.0176 29.8035
I_MB_02 2.6911 0.0038 0.0012 0.9893 34.9514 35.5578
I_MB_01 2.8263 0.0270 0.0226 0.9848 28.6570 29.8839
busStation 3.5297 0.0046 0.0013 0.9826 33.8541 34.4443
I_CA_01 2.6823 0.0025 0.0010 0.9925 36.3298 36.4902
Video AGE pEPs pCEPS MSSSIM PSNR CQM
People&Foliage 34.4507 0.4007 0.3424 0.6085 12.4626 13.4182
boulevardJam 6.0621 0.0723 0.0400 0.8652 25.1959 26.3532
Crowded 9.1140 0.0576 0.0409 0.9569 27.0095 28.3254
tramway 12.3491 0.1341 0.0697 0.7819 19.3411 21.0565
groupCampus 10.9947 0.1391 0.0836 0.8817 23.2991 23.9487
HumanBody2 4.4970 0.0269 0.0122 0.9803 28.1213 28.6049
Board 18.3758 0.2780 0.2382 0.6050 18.4726 19.5750
Foliage 11.7717 0.1740 0.0508 0.9208 23.7278 23.9815
UCF-fishes 1.2660 0.0001 0.0000 0.9807 42.5471 44.0118
ICRA3 6.4711 0.0649 0.0510 0.8983 21.5737 22.5094
IndianTraffic3 2.0326 0.0017 0.0005 0.9918 38.5338 39.4770
Video AGE pEPs pCEPS MSSSIM PSNR CQM
I_IL_01 4.9630 0.0037 0.0022 0.9932 32.2041 33.0205
I_IL_02 4.9075 0.0012 0.0000 0.9933 32.0051 32.8486
Dataset3Camera1 3.1535 0.0033 0.0006 0.9904 34.9628 35.6600
CameraParameter 4.9567 0.0493 0.0306 0.9781 26.5173 27.2836
Dataset3Camera2 2.7060 0.0032 0.0000 0.9921 35.4059 36.0338
cubicle 14.7422 0.1268 0.1001 0.9002 16.1609 17.4362
Video AGE pEPs pCEPS MSSSIM PSNR CQM
PedAndStorrowDrive3 2.9771 0.0146 0.0008 0.9929 32.9840 33.8105
PedAndStorrowDrive 3.3351 0.0087 0.0015 0.9928 31.1373 32.1729
BusStopMorning 6.0792 0.0233 0.0007 0.9857 29.2522 29.9708
Dataset4Camera1 2.0318 0.0011 0.0000 0.9962 37.6945 37.8536
Terrace 5.4027 0.0064 0.0000 0.9780 31.5947 32.3007
Video AGE pEPs pCEPS MSSSIM PSNR CQM
Toscana 10.1748 0.1119 0.0804 0.8566 20.6871 21.6768
CUHK_Square 5.1866 0.0384 0.0015 0.9628 29.3726 29.8880
pedestrians 1.8056 0.0004 0.0000 0.9949 39.3524 39.7823
NoisyNight 4.9072 0.0170 0.0010 0.9353 30.9954 31.9197
peopleInShade 6.6691 0.0564 0.0330 0.9392 24.9800 25.9735
DynamicBackground 7.4114 0.0553 0.0007 0.9615 27.3600 28.0085
snowFall 2.6287 0.0012 0.0001 0.9574 37.0738 37.4932
TwoLeaveShop1cor 4.3521 0.0240 0.0135 0.9415 26.4613 27.1666
MIT 4.4850 0.0288 0.0020 0.9753 29.8452 30.9793
TownCentre 4.3162 0.0121 0.0040 0.9633 31.9081 31.9698