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Background subtraction based on a Self-Adjusting MoG

Year 2019, Volume 2, Issue 1, 73 - 84, 23.09.2019

Abstract

The diversity in background scenes such as, illumination changes, dynamics of the background, camouflage effect, shadow, etc. is a big deal for moving objects detection methods makes it impossible to manage the multimodality of scenes in video surveillance systems. In this paper we present a new method that allows better detection of moving objects. This method combine the robustness of the Artificial Immune Recognition System (AIRS) with respect to the local variations and the power of Gaussian mixtures (MoG) to model changes at the pixel level. The task of the AIRS is to generate several MoG models for each pixel. This models are filtred through two mecanism: the competition for resources and the development of a candidate memory cell. The best model is merged with the exesting MoG according to the Memory cell introduction process. Obtained results on the Wallflower dataset proved the performance of our system compared to other state-of-the-art methods.

References

  • Reference1 Allebosch, G., Van Hamme, D., Deboeverie, F., Veelaert, P., Philips, W.: C-effic: color and edge based foreground background segmentation with interior classification. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics. pp. 433-454. Springer (2015)
  • Reference2 Babaee, M., Dinh, D.T., Rigoll, G.: A deep convolutional neural network for video sequence background subtraction. Pattern Recognition 76, 635-649 (2018)
  • Reference3 Bouwmans, T.: Background subtraction for visual surveillance: A fuzzy approach. Handbook on soft computing for video surveillance 5, 103-138 (2012)
  • Reference4 Brutzer, S., Hoferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: CVPR 2011. pp. 1937-1944. IEEE (2011)
  • Reference5 Bucak, S.S., Gunsel, B.: Video content representation by incremental non-negative matrix factorization. In: 2007 IEEE International Conference on Image Processing. vol. 2, pp. II-113. IEEE (2007)
  • Reference6 Charoenpong, T., Supasuteekul, A., Nuthong, C.: Adaptive background modeling from an image sequence by using k-means clustering. In: ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. pp. 880-883. IEEE (2010)
  • Reference7 Chen, M., Wei, X., Yang, Q., Li, Q., Wang, G., Yang, M.H.: Spatiotemporal gmm for background subtraction with superpixel hierarchy. IEEE transactions on pattern analysis and machine intelligence 40(6), 1518-1525 (2018)
  • Reference8 Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: European conference on computer vision. pp. 751-767. Springer (2000)
  • Reference9 Farou, B., Kouahla, M.N., Seridi, H., Akdag, H.: Effecient local monitoring approach for the task of background subtraction. Engineering Applications of Artificial Intelligence 64, 1-12 (2017)
  • Reference10 Friedman, N., Russell, S.: Image segmentation in video sequences: A probabilistic approach. In: Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence. pp. 175-181. Morgan Kaufmann Publishers Inc. (1997)
  • Reference11 Haritaoglu, I., Harwood, D., Davis, L.S.: W/sup 4: real-time surveillance of people and their activities. IEEE Transactions on pattern analysis and machine intelligence 22(8), 809{830 (2000)
  • Reference12 Hunziker, S., Quanz, S.P., Amara, A., Meyer, M.R.: Pca-based approach for subtracting thermal background emission in high-contrast imaging data. Astronomy & Astrophysics 611, A23 (2018)
  • Reference13 Javed, S., Narayanamurthy, P., Bouwmans, T., Vaswani, N.: Robust pca and robust subspace tracking: A comparative evaluation. In: 2018 IEEE Statistical Signal Processing Workshop (SSP). pp. 836-840. IEEE (2018)
  • Reference14 Jianzhao, C., Victor, O.C., Gilbert, O.M., Changtao, W.: A fast background subtraction method using kernel density estimation for people counting. In: 2017 9th International Conference on Modelling, Identi_cation and Control (ICMIC). pp. 133-138. IEEE (2017)
  • Reference15 Krungkaew, R., Kusakunniran, W.: Foreground segmentation in a video by using a novel dynamic codebook. In: 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). pp. 1-6. IEEE (2016)
  • Reference16 Li, X., Hu, W., Zhang, Z., Zhang, X.: Robust foreground segmentation based on two effective background models. In: Proceedings of the 1st ACM international conference on Multimedia information retrieval. pp. 223-228. ACM (2008)
  • Reference17 Lim, L.A., Keles, H.Y.: Foreground segmentation using convolutional neural networks for multiscale feature encoding. Pattern Recognition Letters 112, 256-262 (2018)
  • Reference18 Lim, L.A., Keles, H.Y.: Learning multi-scale features for foreground segmentation. arXiv preprint arXiv:1808.01477 (2018)
  • Reference19 Martins, I., Carvalho, P., Corte-Real, L., Alba-Castro, J.L.: Bmog: boosted Gaussian mixture model with controlled complexity. In: Iberian Conference on Pattern Recognition and Image Analysis. pp. 50-57. Springer (2017)
  • Reference20 Oliver, N.M., Rosario, B., Pentland, A.P.: A bayesian computer vision system for modeling human interactions. IEEE transactions on pattern analysis and machine intelligence 22(8), 831-843 (2000)
  • Reference21 Shen, Y., Hu, W., Yang, M., Liu, J., Wei, B., Lucey, S., Chou, C.T.: Real-time and robust compressive background subtraction for embedded camera networks. IEEE Transactions on Mobile Computing 15(2), 406-418 (2016)
  • Reference22 St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: A self-adjusting approach to change detection based on background word consensus. In: 2015 IEEE Winter Conference on Applications of Computer Vision. pp. 990-997. IEEE (2015)
  • Reference23 St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Subsense: A universal change detection method with local adaptive sensitivity. IEEE Transactions on Image Processing 24(1), 359-373 (2015)
  • Reference24 Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149). vol. 2, pp. 246-252. IEEE (1999)
  • Reference25 Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallower: Principles and practice of background maintenance. In: Proceedings of the Seventh IEEE International Conference on Computer Vision. vol. 1, pp. 255-261. IEEE (1999)
  • Reference26 Tsai, D.M., Lai, S.C.: Independent component analysis-based background subtraction for indoor surveillance. IEEE Transactions on image processing 18(1), 158-167 (2009)
  • Reference27 Viswanath, A., Behera, R.K., Senthamilarasu, V., Kutty, K.: Background modelling from a moving camera. Procedia Computer Science 58, 289-296 (2015)
  • Reference28 Wang, K., Gou, C., Wang, F.Y.: mf4gcd: A robust change detection method for intelligent visual surveillance. IEEE Access 6, 15505-15520 (2018)
  • Reference29 Wang, Y., Luo, Z., Jodoin, P.M.: Interactive deep learning method for segmenting moving objects. Pattern Recognition Letters 96, 66-75 (2017)
  • Reference30 Watkins, A., Boggess, L.: A new classifier based on resource limited artificial immune systems. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). vol. 2, pp. 1546-1551. IEEE (2002)
  • Reference31 Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: P_nder: Real-time tracking of the human body. IEEE Transactions on pattern analysis and machine intelligence 19(7), 780-785 (1997)
  • Reference32 Xia, H., Song, S., He, L.: A modified gaussian mixture background model via spatiotemporal distribution with shadow detection. Signal, Image and Video Processing 10(2), 343-350 (2016)
  • Reference33 Yu, J., Zhou, X., Qian, F.: Object kinematic model: A novel approach of adaptive background mixture models for video segmentation. In: 2010 8th World Congress on Intelligent Control and Automation. pp. 6225-6228. IEEE (2010)

A New Process for Background Subtraction based on AIRS

Year 2019, Volume 2, Issue 1, 73 - 84, 23.09.2019

Abstract

The diversity in background scenes such as, illumination changes, dynamics of the background, camouflage effect, shadow, etc. is a big deal for moving objects detection methods makes it impossible to manage the multimodality of scenes in video surveillance systems.

In this paper we present a new method that allows better detection of moving objects. This method combine the robustness of the Artificial Immune Recognition System (AIRS) with respect to the local variations and the power of Gaussian mixtures (GMM) to model changes at the pixel level.

The task of the AIRS is to generate several GMM models for each pixel. This models are filtred through two mecanism: the competition for resources and the development of a candidate memory cell. The best model is merged with the exesting GMM according to the Memory cell introduction process.

results obtained on the Wallflower dataset proved the performance of our system compared to other state-of-the-art methods.


References

  • Reference1 Allebosch, G., Van Hamme, D., Deboeverie, F., Veelaert, P., Philips, W.: C-effic: color and edge based foreground background segmentation with interior classification. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics. pp. 433-454. Springer (2015)
  • Reference2 Babaee, M., Dinh, D.T., Rigoll, G.: A deep convolutional neural network for video sequence background subtraction. Pattern Recognition 76, 635-649 (2018)
  • Reference3 Bouwmans, T.: Background subtraction for visual surveillance: A fuzzy approach. Handbook on soft computing for video surveillance 5, 103-138 (2012)
  • Reference4 Brutzer, S., Hoferlin, B., Heidemann, G.: Evaluation of background subtraction techniques for video surveillance. In: CVPR 2011. pp. 1937-1944. IEEE (2011)
  • Reference5 Bucak, S.S., Gunsel, B.: Video content representation by incremental non-negative matrix factorization. In: 2007 IEEE International Conference on Image Processing. vol. 2, pp. II-113. IEEE (2007)
  • Reference6 Charoenpong, T., Supasuteekul, A., Nuthong, C.: Adaptive background modeling from an image sequence by using k-means clustering. In: ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology. pp. 880-883. IEEE (2010)
  • Reference7 Chen, M., Wei, X., Yang, Q., Li, Q., Wang, G., Yang, M.H.: Spatiotemporal gmm for background subtraction with superpixel hierarchy. IEEE transactions on pattern analysis and machine intelligence 40(6), 1518-1525 (2018)
  • Reference8 Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: European conference on computer vision. pp. 751-767. Springer (2000)
  • Reference9 Farou, B., Kouahla, M.N., Seridi, H., Akdag, H.: Effecient local monitoring approach for the task of background subtraction. Engineering Applications of Artificial Intelligence 64, 1-12 (2017)
  • Reference10 Friedman, N., Russell, S.: Image segmentation in video sequences: A probabilistic approach. In: Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence. pp. 175-181. Morgan Kaufmann Publishers Inc. (1997)
  • Reference11 Haritaoglu, I., Harwood, D., Davis, L.S.: W/sup 4: real-time surveillance of people and their activities. IEEE Transactions on pattern analysis and machine intelligence 22(8), 809{830 (2000)
  • Reference12 Hunziker, S., Quanz, S.P., Amara, A., Meyer, M.R.: Pca-based approach for subtracting thermal background emission in high-contrast imaging data. Astronomy & Astrophysics 611, A23 (2018)
  • Reference13 Javed, S., Narayanamurthy, P., Bouwmans, T., Vaswani, N.: Robust pca and robust subspace tracking: A comparative evaluation. In: 2018 IEEE Statistical Signal Processing Workshop (SSP). pp. 836-840. IEEE (2018)
  • Reference14 Jianzhao, C., Victor, O.C., Gilbert, O.M., Changtao, W.: A fast background subtraction method using kernel density estimation for people counting. In: 2017 9th International Conference on Modelling, Identi_cation and Control (ICMIC). pp. 133-138. IEEE (2017)
  • Reference15 Krungkaew, R., Kusakunniran, W.: Foreground segmentation in a video by using a novel dynamic codebook. In: 2016 13th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). pp. 1-6. IEEE (2016)
  • Reference16 Li, X., Hu, W., Zhang, Z., Zhang, X.: Robust foreground segmentation based on two effective background models. In: Proceedings of the 1st ACM international conference on Multimedia information retrieval. pp. 223-228. ACM (2008)
  • Reference17 Lim, L.A., Keles, H.Y.: Foreground segmentation using convolutional neural networks for multiscale feature encoding. Pattern Recognition Letters 112, 256-262 (2018)
  • Reference18 Lim, L.A., Keles, H.Y.: Learning multi-scale features for foreground segmentation. arXiv preprint arXiv:1808.01477 (2018)
  • Reference19 Martins, I., Carvalho, P., Corte-Real, L., Alba-Castro, J.L.: Bmog: boosted Gaussian mixture model with controlled complexity. In: Iberian Conference on Pattern Recognition and Image Analysis. pp. 50-57. Springer (2017)
  • Reference20 Oliver, N.M., Rosario, B., Pentland, A.P.: A bayesian computer vision system for modeling human interactions. IEEE transactions on pattern analysis and machine intelligence 22(8), 831-843 (2000)
  • Reference21 Shen, Y., Hu, W., Yang, M., Liu, J., Wei, B., Lucey, S., Chou, C.T.: Real-time and robust compressive background subtraction for embedded camera networks. IEEE Transactions on Mobile Computing 15(2), 406-418 (2016)
  • Reference22 St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: A self-adjusting approach to change detection based on background word consensus. In: 2015 IEEE Winter Conference on Applications of Computer Vision. pp. 990-997. IEEE (2015)
  • Reference23 St-Charles, P.L., Bilodeau, G.A., Bergevin, R.: Subsense: A universal change detection method with local adaptive sensitivity. IEEE Transactions on Image Processing 24(1), 359-373 (2015)
  • Reference24 Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149). vol. 2, pp. 246-252. IEEE (1999)
  • Reference25 Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallower: Principles and practice of background maintenance. In: Proceedings of the Seventh IEEE International Conference on Computer Vision. vol. 1, pp. 255-261. IEEE (1999)
  • Reference26 Tsai, D.M., Lai, S.C.: Independent component analysis-based background subtraction for indoor surveillance. IEEE Transactions on image processing 18(1), 158-167 (2009)
  • Reference27 Viswanath, A., Behera, R.K., Senthamilarasu, V., Kutty, K.: Background modelling from a moving camera. Procedia Computer Science 58, 289-296 (2015)
  • Reference28 Wang, K., Gou, C., Wang, F.Y.: mf4gcd: A robust change detection method for intelligent visual surveillance. IEEE Access 6, 15505-15520 (2018)
  • Reference29 Wang, Y., Luo, Z., Jodoin, P.M.: Interactive deep learning method for segmenting moving objects. Pattern Recognition Letters 96, 66-75 (2017)
  • Reference30 Watkins, A., Boggess, L.: A new classifier based on resource limited artificial immune systems. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600). vol. 2, pp. 1546-1551. IEEE (2002)
  • Reference31 Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: P_nder: Real-time tracking of the human body. IEEE Transactions on pattern analysis and machine intelligence 19(7), 780-785 (1997)
  • Reference32 Xia, H., Song, S., He, L.: A modified gaussian mixture background model via spatiotemporal distribution with shadow detection. Signal, Image and Video Processing 10(2), 343-350 (2016)
  • Reference33 Yu, J., Zhou, X., Qian, F.: Object kinematic model: A novel approach of adaptive background mixture models for video segmentation. In: 2010 8th World Congress on Intelligent Control and Automation. pp. 6225-6228. IEEE (2010)

Details

Primary Language English
Subjects Computer Science, Interdisciplinary Application
Journal Section Articles
Authors

Wafa NEBİLİ (Primary Author)
8 Mai 1945 Guelma University
0000-0001-7401-0031
Algeria


Samir HALLACİ This is me
University of Guelma
Algeria


Brahim FAROU
08 Mai 1945 Guelma University
0000-0002-1609-6006
Algeria

Publication Date September 23, 2019
Published in Issue Year 2019, Volume 2, Issue 1

Cite

Bibtex @review { ijiam584686, journal = {International Journal of Informatics and Applied Mathematics}, issn = {}, eissn = {2667-6990}, address = {}, publisher = {International Society of Academicians}, year = {2019}, volume = {2}, pages = {73 - 84}, doi = {}, title = {Background subtraction based on a Self-Adjusting MoG}, key = {cite}, author = {Nebili, Wafa and Hallaci, Samir and Farou, Brahim} }
APA Nebili, W. , Hallaci, S. & Farou, B. (2019). Background subtraction based on a Self-Adjusting MoG . International Journal of Informatics and Applied Mathematics , 2 (1) , 73-84 . Retrieved from https://dergipark.org.tr/en/pub/ijiam/issue/48898/584686
MLA Nebili, W. , Hallaci, S. , Farou, B. "Background subtraction based on a Self-Adjusting MoG" . International Journal of Informatics and Applied Mathematics 2 (2019 ): 73-84 <https://dergipark.org.tr/en/pub/ijiam/issue/48898/584686>
Chicago Nebili, W. , Hallaci, S. , Farou, B. "Background subtraction based on a Self-Adjusting MoG". International Journal of Informatics and Applied Mathematics 2 (2019 ): 73-84
RIS TY - JOUR T1 - Background subtraction based on a Self-Adjusting MoG AU - Wafa Nebili , Samir Hallaci , Brahim Farou Y1 - 2019 PY - 2019 N1 - DO - T2 - International Journal of Informatics and Applied Mathematics JF - Journal JO - JOR SP - 73 EP - 84 VL - 2 IS - 1 SN - -2667-6990 M3 - UR - Y2 - 2019 ER -
EndNote %0 International Journal of Informatics and Applied Mathematics Background subtraction based on a Self-Adjusting MoG %A Wafa Nebili , Samir Hallaci , Brahim Farou %T Background subtraction based on a Self-Adjusting MoG %D 2019 %J International Journal of Informatics and Applied Mathematics %P -2667-6990 %V 2 %N 1 %R %U
ISNAD Nebili, Wafa , Hallaci, Samir , Farou, Brahim . "Background subtraction based on a Self-Adjusting MoG". International Journal of Informatics and Applied Mathematics 2 / 1 (September 2019): 73-84 .
AMA Nebili W. , Hallaci S. , Farou B. Background subtraction based on a Self-Adjusting MoG. IJIAM. 2019; 2(1): 73-84.
Vancouver Nebili W. , Hallaci S. , Farou B. Background subtraction based on a Self-Adjusting MoG. International Journal of Informatics and Applied Mathematics. 2019; 2(1): 73-84.
IEEE W. Nebili , S. Hallaci and B. Farou , "Background subtraction based on a Self-Adjusting MoG", International Journal of Informatics and Applied Mathematics, vol. 2, no. 1, pp. 73-84, Sep. 2019

International Journal of Informatics and Applied Mathematics