Contact nameMarwa JMAL
Contact email[email protected]
Contact university/companyEcole Polytechnique de Tunisie
Method's nameNExBI
ReferenceM.Jmal "Real-time Scene Background Initialization based on Spatio-Temporal Neighborhood Exploration" submitted to journal of multimedia tools and applications.
Processing time30 fps for a 320x240 video with unoptimized c++ code running on a core i6 laptop
Code is available onlineFalse
Parameters K=3 (K=1 for veryShort category), N =16, e=2

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

Video AGE pEPs pCEPS MSSSIM PSNR CQM
skating 3.6905 0.0137 0.0025 0.9650 32.7410 33.7866
wetSnow 3.1697 0.0044 0.0021 0.9644 34.7313 35.2591
I_SI_01 2.2875 0.0013 0.0001 0.9942 38.1499 38.5332
streetCornerAtNight 2.2078 0.0032 0.0021 0.9821 36.8066 37.9124
Hybrid 6.8768 0.0407 0.0030 0.9638 28.5932 29.1560
511 5.8916 0.0664 0.0049 0.9345 26.2599 28.3762
Blurred 2.5863 0.0013 0.0002 0.9909 36.3266 36.6845
CamouflageFgObjects 3.6989 0.0076 0.0000 0.9851 33.1042 33.4846
IntelligentRoom 4.2122 0.0051 0.0000 0.9886 32.8771 33.3042
PETS2006 2.2332 0.0049 0.0029 0.9822 33.2468 34.0063
IPPR2 8.1399 0.0527 0.0316 0.8500 23.6965 24.3852
MPEG4_40 2.9795 0.0202 0.0026 0.9751 31.4684 32.5402
ComplexBackground 6.9864 0.0457 0.0017 0.9809 28.0525 28.7295
Intersection 2.8774 0.0043 0.0010 0.9766 34.8663 35.4716
fluidHighway 9.4463 0.0609 0.0356 0.9112 26.4894 26.7421
highway 8.6614 0.0428 0.0023 0.9628 26.5751 27.7318
Video AGE pEPs pCEPS MSSSIM PSNR CQM
overpass 8.7877 0.1334 0.0140 0.8531 23.8343 24.9767
advertisementBoard 2.6212 0.0039 0.0015 0.9882 36.4789 36.6791
canoe 18.8497 0.3200 0.0762 0.6245 18.4152 19.0610
fountain01 7.2796 0.0798 0.0183 0.9046 23.8574 25.1074
fountain02 6.2824 0.0527 0.0046 0.9336 28.2512 29.1400
fall 25.6899 0.3561 0.1128 0.7044 15.3712 16.4542
Video AGE pEPs pCEPS MSSSIM PSNR CQM
O_SM_04 8.5433 0.0992 0.0077 0.9139 24.8194 26.0367
boulevard 9.4182 0.1228 0.0158 0.9076 22.2455 23.7767
I_SM_04 3.4690 0.0182 0.0007 0.9849 31.6923 32.5510
I_MC_02 16.0678 0.2021 0.0793 0.7142 18.7041 19.9225
badminton 5.2289 0.0509 0.0189 0.8726 26.7733 27.7054
O_MC_02 15.4115 0.2020 0.0912 0.7440 18.9209 19.7796
traffic 11.3764 0.1315 0.0703 0.7832 23.0228 24.0855
CMU 5.6560 0.0511 0.0014 0.9875 28.0431 28.8575
sidewalk 25.0000 0.3212 0.1764 0.4139 15.3708 17.1426
Video AGE pEPs pCEPS MSSSIM PSNR CQM
Uturn 3.9561 0.0263 0.0147 0.9612 28.0866 28.7615
CaVignal 1.5242 0.0005 0.0000 0.9949 39.8208 40.1792
office 5.3003 0.0100 0.0020 0.9914 31.1749 32.0706
sofa 5.2819 0.0402 0.0279 0.9050 23.0706 24.2798
copyMachine 7.2364 0.0288 0.0134 0.9614 28.4363 29.4852
tramstop 5.7088 0.0381 0.0024 0.9795 28.6040 29.3420
UCF-traffic 3.0636 0.0270 0.0115 0.9288 29.8298 31.2969
streetCorner 5.9160 0.0306 0.0150 0.9647 26.6364 27.6667
AVSS2007 12.3242 0.0947 0.0697 0.8799 21.1518 22.0076
Teknomo 6.9666 0.0479 0.0097 0.9552 25.3235 26.3857
I_CA_02 2.8526 0.0095 0.0022 0.9824 32.4404 33.0889
Candela_m1.10 2.6429 0.0023 0.0000 0.9918 35.8763 35.7851
I_MB_02 2.3171 0.0030 0.0017 0.9842 32.8445 33.6649
I_MB_01 2.9121 0.0271 0.0228 0.9845 28.6272 29.8269
busStation 3.0622 0.0030 0.0007 0.9815 35.2212 35.7016
I_CA_01 3.1337 0.0074 0.0027 0.9765 34.4332 34.6154
Video AGE pEPs pCEPS MSSSIM PSNR CQM
People&Foliage 1.7570 0.0002 0.0000 0.9971 39.7489 39.7312
boulevardJam 5.0516 0.0514 0.0170 0.8789 27.6165 28.8454
Crowded 8.6862 0.0550 0.0328 0.9391 27.2461 28.5195
tramway 12.4111 0.1599 0.0331 0.8374 20.5096 22.0177
groupCampus 7.5163 0.0678 0.0325 0.9274 27.4683 28.5439
HumanBody2 4.2218 0.0221 0.0021 0.9891 30.1015 30.6972
Board 6.7738 0.0459 0.0117 0.9162 28.1156 29.0466
Foliage 3.6524 0.0037 0.0000 0.9949 33.8375 34.2265
UCF-fishes 2.5228 0.0166 0.0089 0.9008 29.7060 32.0306
ICRA3 2.3595 0.0065 0.0004 0.9935 35.2942 35.5258
IndianTraffic3 3.4480 0.0266 0.0171 0.9430 30.4174 31.6475
Video AGE pEPs pCEPS MSSSIM PSNR CQM
I_IL_01 3.0329 0.0272 0.0190 0.9898 32.5195 33.3700
I_IL_02 6.2417 0.0872 0.0561 0.9584 26.2575 27.0616
Dataset3Camera1 4.4373 0.0412 0.0217 0.9615 29.0687 30.0467
CameraParameter 2.1851 0.0102 0.0073 0.9756 36.7813 37.5176
Dataset3Camera2 6.8476 0.1070 0.0632 0.9168 23.9731 25.2688
cubicle 6.2415 0.0489 0.0254 0.9702 26.6217 27.4637
Video AGE pEPs pCEPS MSSSIM PSNR CQM
PedAndStorrowDrive3 4.5470 0.0380 0.0048 0.9819 28.2004 29.2183
PedAndStorrowDrive 11.0096 0.1503 0.0363 0.9023 21.6622 23.1940
BusStopMorning 6.9018 0.0440 0.0082 0.9501 25.4774 26.0903
Dataset4Camera1 1.8317 0.0043 0.0002 0.9915 35.8412 36.2144
Terrace 7.0590 0.0617 0.0363 0.8706 22.4357 23.4769
Video AGE pEPs pCEPS MSSSIM PSNR CQM
Toscana 3.8839 0.0154 0.0071 0.9616 29.0250 29.8464
CUHK_Square 5.9819 0.0565 0.0042 0.9451 27.2737 27.8796
pedestrians 2.0927 0.0008 0.0000 0.9936 38.0300 38.5545
NoisyNight 6.4966 0.0388 0.0093 0.8864 27.4191 28.6868
peopleInShade 9.4336 0.0666 0.0490 0.8962 20.4872 21.4974
DynamicBackground 11.8629 0.1707 0.0228 0.9083 22.8422 23.5873
snowFall 3.5263 0.0013 0.0001 0.9407 35.1409 35.5612
TwoLeaveShop1cor 3.6254 0.0095 0.0062 0.9724 27.8484 28.4244
MIT 6.0949 0.0604 0.0067 0.9532 26.7849 28.0079
TownCentre 4.1359 0.0074 0.0006 0.9670 32.8392 32.9182