Overview
The main objective of the 2016 IEEE Scene Background Modeling Contest (SBMC 2016) is to challenge research teams from around the world to test their scene background modeling algorithms on our 8 video categories dataset, namely:
- Basic (16 basic videos),
- Intermittent Motion (16 videos subject to "ghosting" artifacts),
- Clutter (11 videos containing foreground moving objects occluding a large portion of the background),
- Jitter (9 videos shot by a moving camera),
- Illumination Changes (6 videos with strong illumination variations),
- Background Motion (6 videos containing background motion),
- Very Long (5 videos containing thousands of frames),
- Very Short (10 videos of only few frames),
The videos have been selected to cover a wide range of scene background modeling challenges and are representative of typical visual data captured today in surveillance, smart environment, and video database scenarios. This dataset aims to provide a rigorous academic benchmarking facility for testing and validating existing and new algorithms for scene background modeling.
The best performing algorithms submitted to the contest will be invited for oral presentation. Papers will be published in the 2016 ICPR Contest Proceedings. All submissions that meet minimum standards will be reported in the dataset on-line and in an overview-paper associated with the contest. The contest will also include an invited talk.
Key Dates (NEW DATES)
- 16 May (Mon): Registration to the competition is opened.
- 16 May (Mon): Publication of the dataset.
18 July (Thur) 5 August (Fri): Deadline to submit contest results.
19 July (Mon) 5 August (Fri): Deadline to submit contest paper.
5 August (Fri) 12 August (Fri): Notification of acceptance.
- 12 Sep (Mon): Deadline to submit camera ready contest paper.
Rules for participation
- Researchers from both the academia and the industry are welcome to submit results.
- Results must be reported for each video of each category.
- Only one set of tuning parameters should be used for all videos.
- Numerical scores can be computed using Matlab or Python programs available under UTILITIES. Both programs take the output produced by an algorithm and the available ground-truth color image, and compute performance metrics described on the RESULTS page.
- In order for a method to be ranked on this website, upload your results via the UPLOAD page.
- Methods published in the past can be submitted as long as extensive evaluation over all 8 video categories is performed.
- Paper length: max 6 pages.
- Latex/word templates: 3 files available at: HERE.
- Please submit your paper HERE.
Program
14:00 - 14:20 | Opening remarks and description of the challenge |
14:20 - 14:40 | "Evaluation of the Background Modeling Method Auto-Adaptive Parallel Neural Network Architecture in the SBMnet Dataset", M. Chacon-Murguia, G. Ramirez-Alonso and J. Ramirez-Quintana |
14:40 - 15:00 | "Rejection based Multipath Reconstruction for Background Estimation in SBMnet 2016 dataset", D. Ortego, J. C. Sanmiguel and J. M. Martínez |
15:00 - 15:30 | Coffee Break |
15:30 - 15:50 | "CNN-based Initial Background Estimation", I. Halfaoui, F. Bouzaraa and O. Urfalioglu |
15:50 - 16:10 | "Background Initialization based on Bidirectional Analysis and Consensus Voting", T. Minematsu, A. Shimada and R-I Taniguchi |
16:10 - 16:30 | "Scene Background Estimation Based on Temporal Median Filter with Gaussian Filtering", W. Liu, Y. Cai, M. Zhang, H. Li and H. Gu |
16:30 - 16:40 | Break |
16:40 - 17:00 | "Motion-Aware Graph Regularized RPCA for Background Modeling of Complex Scene", S. Javed, A. Mahmood, T. Bouwmans and S.-K. Jung |
17:00 - 17:20 | "Extracting a Background Image by a Multi-modal Scene Background Model", L. Maddalena and A. Petrosino |
17:20 - 17:40 | "LaBGen-P: A Pixel-Level Stationary Background Generation Method Based on LaBGen", B. Laugraud, S. Piérard, M. Van Droogenbroeck |
17:40 - 18:00 | Conclusion and future works |
|
Contest Organizers
Dataset contributors
SBMC 2016 Program Committee
- Thierry Bouwmans, Universitè La Rochelle (France)
- Maurizio Giordano, National Research Council (Italy)
- Pierre-Marc Jodoin, University of Sherbrooke (Canada)
- Zhiming Luo, University of Sherbrooke (Canada)
- Lucia Maddalena, National Research Council (Italy)
- Alfredo Petrosino, University of Naples "Parthenope" (Italy)
- Sébastien Piérard, University of Liège (Belgium)
- Yi Wang, University of Sherbrooke (Canada)
Acknowledgment
- Yi Wang, Ph.D student, Université de Sherbrooke, Canada
Webmaster, software developer
- Martin Cousineau, Université de Sherbrooke, Canada
Webmaster, software developer
Results (September 12, 2016)
Picture of the winner! From left to right, Lucia Maddalena, Benjamin Laugraud (winner!), Pierre-Marc Jodoin
Results, all categories combined.
Click on method name for more details.
LaBGen
[6]
|
2.00 |
4.75 |
6.7090 |
0.0631 |
0.0265 |
0.9266 |
28.6396 |
29.4668 |
LaBGen-P
[7]
|
2.83 |
5.38 |
7.0738 |
0.0706 |
0.0319 |
0.9278 |
28.4660 |
29.3196 |
Photomontage
[3]
|
3.33 |
6.50 |
7.1950 |
0.0686 |
0.0257 |
0.9189 |
28.0113 |
28.8719 |
SC-SOBS-C4
[9]
|
4.33 |
6.00 |
7.5183 |
0.0711 |
0.0242 |
0.9160 |
27.6533 |
28.5601 |
MAGRPCA
[10]
|
5.83 |
6.88 |
8.3132 |
0.0994 |
0.0567 |
0.9401 |
28.4556 |
29.3152 |
Temporal median filter
[2]
|
7.17 |
5.50 |
8.2761 |
0.0984 |
0.0546 |
0.9130 |
27.5364 |
28.4434 |
BE-AAPSA
[14]
|
7.17 |
7.88 |
7.9086 |
0.0873 |
0.0447 |
0.9127 |
27.0714 |
27.9811 |
Bidirectional Analysis
[13]
|
7.67 |
6.63 |
8.3449 |
0.0756 |
0.0181 |
0.9085 |
26.1722 |
27.1637 |
Bidirectional Analysis and Consensus Voting
[12]
|
8.67 |
7.75 |
8.5816 |
0.0724 |
0.0257 |
0.9078 |
26.1018 |
27.1000 |
TMFG
[11]
|
10.00 |
6.25 |
7.4020 |
0.1051 |
0.0566 |
0.9043 |
27.1347 |
28.0530 |
FC-FlowNet
[5]
|
10.17 |
9.00 |
9.1131 |
0.1128 |
0.0599 |
0.9162 |
26.9559 |
27.8767 |
RSL2011
[4]
|
11.17 |
10.25 |
9.0443 |
0.1008 |
0.0497 |
0.8891 |
25.8051 |
26.7986 |
AAPSA
[1]
|
12.17 |
10.88 |
9.2044 |
0.1057 |
0.0523 |
0.9000 |
25.3947 |
26.3021 |
RMR
[8]
|
12.50 |
10.00 |
9.5363 |
0.1176 |
0.0582 |
0.8790 |
26.5217 |
27.4549 |
Results, for the basic category.
Click on method name for more details.
TMFG
[11]
|
1.50 |
3.8063 |
0.0131 |
0.0031 |
0.9803 |
33.7483 |
34.3541 |
Temporal median filter
[2]
|
2.33 |
3.8269 |
0.0139 |
0.0034 |
0.9804 |
33.7085 |
34.3342 |
LaBGen-P
[7]
|
4.00 |
3.9712 |
0.0156 |
0.0041 |
0.9749 |
33.2445 |
33.8797 |
LaBGen
[6]
|
4.33 |
3.9012 |
0.0154 |
0.0045 |
0.9742 |
32.9009 |
33.5733 |
Bidirectional Analysis
[13]
|
4.67 |
4.1075 |
0.0161 |
0.0017 |
0.9736 |
32.3092 |
33.0339 |
Bidirectional Analysis and Consensus Voting
[12]
|
6.00 |
4.1421 |
0.0159 |
0.0019 |
0.9723 |
31.9277 |
32.6950 |
SC-SOBS-C4
[9]
|
6.33 |
4.3598 |
0.0200 |
0.0033 |
0.9728 |
32.1766 |
32.8665 |
Photomontage
[3]
|
6.83 |
4.4856 |
0.0226 |
0.0039 |
0.9719 |
32.3208 |
32.9621 |
RSL2011
[4]
|
9.17 |
4.5546 |
0.0269 |
0.0081 |
0.9660 |
31.6767 |
32.4654 |
FC-FlowNet
[5]
|
10.17 |
5.5856 |
0.0327 |
0.0142 |
0.9636 |
30.4121 |
31.2285 |
BE-AAPSA
[14]
|
11.83 |
5.6842 |
0.0472 |
0.0273 |
0.9626 |
30.1101 |
30.9698 |
MAGRPCA
[10]
|
12.50 |
8.9576 |
0.1124 |
0.0882 |
0.9669 |
29.6430 |
30.4461 |
AAPSA
[1]
|
12.50 |
5.6500 |
0.0449 |
0.0222 |
0.9575 |
29.4528 |
30.1252 |
RMR
[8]
|
12.83 |
5.8867 |
0.0480 |
0.0167 |
0.9400 |
29.6069 |
30.4044 |
Results, for the intermittent motion category.
Click on method name for more details.
LaBGen-P
[7]
|
2.00 |
4.1278 |
0.0225 |
0.0115 |
0.9712 |
31.9974 |
32.7260 |
RMR
[8]
|
2.00 |
4.3606 |
0.0213 |
0.0091 |
0.9730 |
31.1372 |
31.9285 |
Bidirectional Analysis and Consensus Voting
[12]
|
2.67 |
4.5966 |
0.0198 |
0.0079 |
0.9657 |
30.2170 |
31.1074 |
LaBGen
[6]
|
4.67 |
4.4333 |
0.0293 |
0.0164 |
0.9597 |
29.4757 |
30.3373 |
Bidirectional Analysis
[13]
|
4.83 |
5.0569 |
0.0245 |
0.0082 |
0.9533 |
28.6439 |
29.5925 |
RSL2011
[4]
|
6.67 |
5.1116 |
0.0399 |
0.0244 |
0.9532 |
28.0795 |
29.0599 |
SC-SOBS-C4
[9]
|
8.33 |
6.2583 |
0.0487 |
0.0238 |
0.9255 |
25.9249 |
26.9569 |
BE-AAPSA
[14]
|
8.50 |
6.6997 |
0.0547 |
0.0369 |
0.9345 |
26.5072 |
27.5251 |
FC-FlowNet
[5]
|
8.67 |
6.7811 |
0.0599 |
0.0347 |
0.9312 |
27.0272 |
27.9086 |
MAGRPCA
[10]
|
8.83 |
8.3106 |
0.1391 |
0.1018 |
0.9710 |
29.7923 |
30.6718 |
TMFG
[11]
|
10.67 |
6.7602 |
0.0609 |
0.0409 |
0.9167 |
24.8249 |
25.8626 |
Photomontage
[3]
|
11.83 |
7.1460 |
0.0639 |
0.0427 |
0.9138 |
24.8941 |
25.8682 |
Temporal median filter
[2]
|
11.83 |
6.8003 |
0.0615 |
0.0421 |
0.9150 |
24.7016 |
25.7573 |
AAPSA
[1]
|
13.50 |
8.2573 |
0.0768 |
0.0522 |
0.9009 |
24.2814 |
25.2087 |
Results, for the clutter category.
Click on method name for more details.
Photomontage
[3]
|
2.83 |
6.8195 |
0.0543 |
0.0294 |
0.8892 |
28.5554 |
29.4882 |
Bidirectional Analysis
[13]
|
3.17 |
6.6565 |
0.0497 |
0.0177 |
0.9243 |
26.4376 |
27.5267 |
SC-SOBS-C4
[9]
|
4.00 |
7.0590 |
0.0644 |
0.0304 |
0.8939 |
28.0077 |
29.0737 |
LaBGen-P
[7]
|
4.83 |
7.8947 |
0.0986 |
0.0678 |
0.8967 |
28.1140 |
29.1305 |
MAGRPCA
[10]
|
4.83 |
8.1589 |
0.0647 |
0.0294 |
0.9446 |
26.6872 |
27.5988 |
RSL2011
[4]
|
4.83 |
7.3013 |
0.0701 |
0.0375 |
0.9087 |
27.9304 |
28.9763 |
Bidirectional Analysis and Consensus Voting
[12]
|
5.00 |
7.2284 |
0.0546 |
0.0243 |
0.9206 |
25.9467 |
27.0419 |
LaBGen
[6]
|
6.83 |
8.0579 |
0.1035 |
0.0740 |
0.8834 |
26.7690 |
27.7986 |
Temporal median filter
[2]
|
9.83 |
12.5316 |
0.1590 |
0.1108 |
0.8185 |
26.1441 |
27.1507 |
TMFG
[11]
|
10.00 |
11.7469 |
0.1397 |
0.0952 |
0.8144 |
25.9395 |
27.0378 |
FC-FlowNet
[5]
|
10.83 |
12.5556 |
0.1719 |
0.1185 |
0.8762 |
25.3201 |
26.4707 |
BE-AAPSA
[14]
|
11.50 |
12.3049 |
0.1775 |
0.1205 |
0.8526 |
23.7151 |
24.8666 |
RMR
[8]
|
13.00 |
15.3119 |
0.1819 |
0.1202 |
0.7306 |
23.3608 |
24.5341 |
AAPSA
[1]
|
13.50 |
15.7186 |
0.2577 |
0.1863 |
0.8240 |
22.8853 |
23.9875 |
Results, for the jitter category.
Click on method name for more details.
Temporal median filter
[2]
|
1.50 |
9.0892 |
0.1063 |
0.0404 |
0.8556 |
25.5526 |
26.6236 |
TMFG
[11]
|
2.67 |
9.2013 |
0.1069 |
0.0408 |
0.8543 |
25.4488 |
26.5210 |
LaBGen-P
[7]
|
4.33 |
9.6487 |
0.1100 |
0.0410 |
0.8504 |
25.2447 |
26.2834 |
LaBGen
[6]
|
5.33 |
9.7096 |
0.1108 |
0.0420 |
0.8487 |
25.3282 |
26.3608 |
Bidirectional Analysis
[13]
|
7.00 |
10.1835 |
0.1090 |
0.0341 |
0.8385 |
23.6701 |
24.8364 |
SC-SOBS-C4
[9]
|
7.17 |
10.0232 |
0.1186 |
0.0420 |
0.8403 |
24.5562 |
25.6570 |
Bidirectional Analysis and Consensus Voting
[12]
|
7.50 |
10.0040 |
0.1098 |
0.0365 |
0.8369 |
23.5048 |
24.6902 |
FC-FlowNet
[5]
|
7.67 |
10.2805 |
0.1138 |
0.0446 |
0.8489 |
25.0240 |
26.0705 |
MAGRPCA
[10]
|
8.00 |
10.9525 |
0.1131 |
0.0419 |
0.8503 |
24.4999 |
25.6199 |
AAPSA
[1]
|
8.17 |
10.2185 |
0.1202 |
0.0382 |
0.8545 |
23.2861 |
24.2624 |
Photomontage
[3]
|
9.17 |
10.1272 |
0.1210 |
0.0441 |
0.8390 |
24.3478 |
25.4186 |
BE-AAPSA
[14]
|
10.00 |
10.1994 |
0.1246 |
0.0584 |
0.8373 |
24.5489 |
25.6301 |
RSL2011
[4]
|
12.50 |
10.5876 |
0.1237 |
0.0493 |
0.8059 |
22.6947 |
23.8316 |
RMR
[8]
|
14.00 |
11.5991 |
0.1468 |
0.0624 |
0.7806 |
22.1418 |
23.2998 |
Results, for the illumination changes category.
Click on method name for more details.
Photomontage
[3]
|
1.50 |
5.2668 |
0.0329 |
0.0155 |
0.9743 |
30.2102 |
31.0393 |
LaBGen
[6]
|
2.67 |
6.1922 |
0.0440 |
0.0206 |
0.9725 |
29.7108 |
30.5510 |
AAPSA
[1]
|
3.67 |
6.7259 |
0.0562 |
0.0250 |
0.9728 |
28.5080 |
29.2912 |
MAGRPCA
[10]
|
5.00 |
7.7987 |
0.1158 |
0.0804 |
0.9760 |
31.9554 |
32.7325 |
RMR
[8]
|
5.00 |
7.1869 |
0.0656 |
0.0403 |
0.9485 |
28.7961 |
29.8049 |
LaBGen-P
[7]
|
5.83 |
7.4945 |
0.0611 |
0.0376 |
0.9630 |
25.2155 |
26.3522 |
BE-AAPSA
[14]
|
5.83 |
7.0447 |
0.0694 |
0.0451 |
0.9613 |
27.4897 |
28.2828 |
SC-SOBS-C4
[9]
|
8.33 |
10.3591 |
0.1005 |
0.0574 |
0.9075 |
26.2190 |
27.0837 |
RSL2011
[4]
|
9.50 |
9.1963 |
0.0996 |
0.0669 |
0.9349 |
23.9579 |
25.1714 |
Temporal median filter
[2]
|
10.17 |
12.2205 |
0.2322 |
0.1783 |
0.9400 |
24.3156 |
25.3760 |
FC-FlowNet
[5]
|
10.83 |
13.3662 |
0.2584 |
0.1906 |
0.9452 |
24.2047 |
25.2306 |
Bidirectional Analysis and Consensus Voting
[12]
|
11.17 |
16.1236 |
0.1192 |
0.0668 |
0.8653 |
21.4330 |
22.5940 |
Bidirectional Analysis
[13]
|
12.00 |
16.8302 |
0.1833 |
0.0417 |
0.8252 |
19.3961 |
20.6819 |
TMFG
[11]
|
13.50 |
22.0886 |
0.3025 |
0.2119 |
0.8612 |
20.8670 |
22.0259 |
Results, for the background motion category.
Click on method name for more details.
TMFG
[11]
|
1.33 |
9.0773 |
0.1185 |
0.0228 |
0.8706 |
26.4052 |
27.2770 |
BE-AAPSA
[14]
|
2.17 |
9.3755 |
0.1266 |
0.0259 |
0.8766 |
26.0041 |
26.9062 |
Temporal median filter
[2]
|
4.17 |
9.6479 |
0.1262 |
0.0300 |
0.8566 |
25.9277 |
26.7931 |
MAGRPCA
[10]
|
4.50 |
10.0742 |
0.1318 |
0.0299 |
0.8748 |
25.8756 |
26.7832 |
FC-FlowNet
[5]
|
5.33 |
10.0539 |
0.1440 |
0.0362 |
0.8612 |
26.0302 |
26.9014 |
Bidirectional Analysis and Consensus Voting
[12]
|
6.17 |
10.5260 |
0.1316 |
0.0269 |
0.8553 |
25.2106 |
26.1305 |
LaBGen
[6]
|
7.50 |
10.4996 |
0.1356 |
0.0334 |
0.8465 |
25.4982 |
26.1616 |
LaBGen-P
[7]
|
7.67 |
10.5858 |
0.1350 |
0.0336 |
0.8442 |
25.7879 |
26.5748 |
Bidirectional Analysis
[13]
|
7.83 |
10.7772 |
0.1350 |
0.0273 |
0.8574 |
24.0802 |
25.0203 |
SC-SOBS-C4
[9]
|
8.67 |
10.7280 |
0.1481 |
0.0302 |
0.8486 |
24.5806 |
25.5603 |
AAPSA
[1]
|
10.67 |
11.1404 |
0.1488 |
0.0381 |
0.8422 |
24.4876 |
25.4679 |
Photomontage
[3]
|
12.67 |
12.0930 |
0.1589 |
0.0410 |
0.8244 |
23.5420 |
24.5253 |
RMR
[8]
|
12.83 |
12.2932 |
0.1682 |
0.0464 |
0.8151 |
23.9116 |
24.7040 |
RSL2011
[4]
|
13.50 |
13.2090 |
0.1604 |
0.0531 |
0.8040 |
23.8834 |
24.7017 |
Results, for the very long category.
Click on method name for more details.
BE-AAPSA
[14]
|
1.33 |
3.8745 |
0.0148 |
0.0010 |
0.9844 |
32.5501 |
33.1853 |
MAGRPCA
[10]
|
2.17 |
3.9164 |
0.0184 |
0.0040 |
0.9861 |
31.3101 |
32.0265 |
SC-SOBS-C4
[9]
|
4.50 |
6.0638 |
0.0355 |
0.0021 |
0.9837 |
29.2615 |
30.1014 |
LaBGen
[6]
|
4.67 |
5.5689 |
0.0327 |
0.0075 |
0.9807 |
29.2590 |
30.1356 |
Temporal median filter
[2]
|
5.83 |
6.9588 |
0.0557 |
0.0199 |
0.9843 |
29.3160 |
30.2107 |
Photomontage
[3]
|
7.33 |
6.6446 |
0.0629 |
0.0259 |
0.9838 |
29.2081 |
30.0166 |
Bidirectional Analysis
[13]
|
7.33 |
6.7502 |
0.0395 |
0.0012 |
0.9675 |
27.8260 |
28.7710 |
TMFG
[11]
|
7.50 |
7.3530 |
0.0671 |
0.0274 |
0.9851 |
29.2432 |
30.1213 |
Bidirectional Analysis and Consensus Voting
[12]
|
9.00 |
7.0049 |
0.0429 |
0.0045 |
0.9558 |
27.6839 |
28.6442 |
AAPSA
[1]
|
9.00 |
6.6297 |
0.0547 |
0.0129 |
0.9614 |
27.4929 |
28.3462 |
FC-FlowNet
[5]
|
9.33 |
7.7727 |
0.0673 |
0.0189 |
0.9690 |
28.6840 |
29.5294 |
LaBGen-P
[7]
|
10.00 |
7.3028 |
0.0803 |
0.0395 |
0.9760 |
28.2974 |
29.1409 |
RMR
[8]
|
13.33 |
13.2459 |
0.2547 |
0.1571 |
0.9115 |
25.1309 |
26.0738 |
RSL2011
[4]
|
13.67 |
13.2990 |
0.1980 |
0.1170 |
0.8544 |
23.9750 |
25.0400 |
Results, for the very short category.
Click on method name for more details.
Photomontage
[3]
|
1.50 |
4.9770 |
0.0327 |
0.0030 |
0.9548 |
31.0117 |
31.6568 |
Temporal median filter
[2]
|
2.50 |
5.1336 |
0.0324 |
0.0120 |
0.9537 |
30.6255 |
31.3012 |
TMFG
[11]
|
2.83 |
5.1825 |
0.0321 |
0.0106 |
0.9520 |
30.6010 |
31.2242 |
SC-SOBS-C4
[9]
|
3.17 |
5.2953 |
0.0330 |
0.0044 |
0.9556 |
30.4997 |
31.1813 |
LaBGen
[6]
|
5.33 |
5.3093 |
0.0332 |
0.0136 |
0.9474 |
30.1754 |
30.8162 |
LaBGen-P
[7]
|
6.50 |
5.5653 |
0.0417 |
0.0199 |
0.9461 |
29.8264 |
30.4694 |
FC-FlowNet
[5]
|
8.00 |
6.5094 |
0.0541 |
0.0211 |
0.9345 |
28.9447 |
29.6743 |
Bidirectional Analysis
[13]
|
8.17 |
6.3972 |
0.0480 |
0.0126 |
0.9279 |
27.0145 |
27.8472 |
RMR
[8]
|
8.17 |
6.4059 |
0.0541 |
0.0137 |
0.9329 |
28.0882 |
28.8900 |
MAGRPCA
[10]
|
10.33 |
8.3363 |
0.0999 |
0.0778 |
0.9511 |
27.8815 |
28.6430 |
BE-AAPSA
[14]
|
10.83 |
8.0857 |
0.0832 |
0.0429 |
0.8921 |
25.6458 |
26.5128 |
Bidirectional Analysis and Consensus Voting
[12]
|
11.83 |
9.0271 |
0.0850 |
0.0366 |
0.8902 |
22.8906 |
23.8972 |
RSL2011
[4]
|
12.50 |
9.0950 |
0.0877 |
0.0410 |
0.8859 |
24.2435 |
25.1426 |
AAPSA
[1]
|
13.33 |
9.2952 |
0.0860 |
0.0438 |
0.8870 |
22.7636 |
23.7275 |