A New Process for Selecting the Best Background Representatives based on GMM

Yıl 2018, Cilt: 1 Sayı: 1, 35 - 46, 20.12.2018

Nebili Wafa Seridi Hamid Kouahla Mohamed Nadjib

Öz

Background subtraction is an essential step in the process of monitoring videos. Several works have been proposed to differentiate the background pixels from the foreground pixels. Mixtures of Gaussian (GMM) are among the most popular models for a such problem. However, they suffer from some inconveniences related to the light variations and complex scene. In this paper, we propose an improvement of the GMM by proposing a new technique of ordering the Gaussian distributions in the selection phase of the best representatives of the scene. Our approach replaces the usual ranking of Gaussians according to the value of w_k ,t/σ_t  with sorting according to their covariance measure which is calculated between each pixel and each of these Gaussians. the obtained results on the Wallflower dataset has proven the effectiveness of the proposed approach compared to standard GMM.

Anahtar Kelimeler

GMM, Video surveillance, Background Subtraction

Kaynakça

• Reference1 Azab, M.M., Shedeed, H.A., Hussein, A.S.: A new technique for background modeling and subtraction for motion detection in real-time videos. In: Image Processing (ICIP), 2010 17th IEEE International Conference on. pp. 3453-3456. IEEE (2010)
• Reference2 Bhaskar, H., Mihaylova, L., Maskell, S.: Automatic target detection based on background modeling using adaptive cluster density estimation (2007)
• Reference3 Bouwmans, T., El Baf, F.: Modeling of dynamic backgrounds by type-2 fuzzy gaussians mixture models. MASAUM Journal of of Basic and Applied Sciences 1(2), 265-276 (2010)
• Reference 4 Bucak, S.S., Gunsel, B.: Video content representation by incremental non-negative matrix factorization. In: Image Processing, 2007. ICIP 2007. IEEE International Conference on. vol. 2, pp. II-113. IEEE (2007)
• Reference5 Caseiro, R., Henriques, J.F., Batista, J.: Foreground segmentation via background modeling on riemannian manifolds. In: Pattern Recognition (ICPR), 2010 20th International Conference on. pp. 3570-3574. IEEE (2010)
• Reference6 Charoenpong, T., Supasuteekul, A., Nuthong, C.: Adaptive background modeling from an image sequence by using k-means clustering. In: Electrical Engineering/ Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on. pp. 880-883. IEEE (2010)
• Reference7 Collins, R.T., Lipton, A.J., Kanade, T., Fujiyoshi, H., Duggins, D., Tsin, Y., Tolliver, D., Enomoto, N., Hasegawa, O., Burt, P., et al.: A system for video surveillance and monitoring. VSAM final report pp. 1-68 (2000)
• Reference8 Doulamis, A., Kalisperakis, I., Stentoumis, C., Matsatsinis, N.: Self adaptive background modeling for identifying persons' falls. In: Semantic Media Adaptation and Personalization (SMAP), 2010 5th International Workshop on. pp. 57-63. IEEE (2010)
• Reference9 El Baf, F., Bouwmans, T., Vachon, B.: Fuzzy integral for moving object detection. In: Fuzzy Systems, 2008. FUZZ-IEEE 2008.(IEEE World Congress on Computational Intelligence). IEEE International Conference on. pp. 1729-1736. IEEE (2008)
• Reference10 El Baf, F., Bouwmans, T., Vachon, B.: Type-2 fuzzy mixture of gaussians model: application to background modeling. In: International Symposium on Visual Computing. pp. 772-781. Springer (2008)
• Reference11 El Baf, F., Bouwmans, T., Vachon, B.: Fuzzy statistical modeling of dynamic backgrounds for moving object detection in infrared videos. In: Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on. pp. 60-65. IEEE (2009)
• Reference12 Elgammal, A., Harwood, D., Davis, L.: Non-parametric model for background subtraction. In: European conference on computer vision. pp. 751-767. Springer (2000)
• Reference13 Fang, X., Xiong, W., Hu, B., Wang, L.: A moving object detection algorithm based on color information. In: Journal of Physics: Conference Series. vol. 48, p. 384. IOP Publishing (2006)
• Reference14 Farou, B., Kouahla, M.N., Seridi, H., Akdag, H.: Efficient local monitoring approach for the task of background subtraction. Engineering Applications of Artificial Intelligence 64, 1-12 (2017)
• Reference15 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)
• Reference16 Haritaoglu, I., Harwood, D., Davis, L.S.: W4: Real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis & Machine Intelligence (8), 809-830 (2000)
• Reference17 Hayman, E., Eklundh, J.O.: Statistical background subtraction for a mobile observer. In: null. p. 67. IEEE (2003)
• Reference18 Jain, V., Kimia, B.B., Mundy, J.L.: Background modeling based on subpixel edges. In: Image Processing, 2007. ICIP 2007. IEEE International Conference on. vol. 6, pp. VI-321. IEEE (2007)
• Reference19 Jian, X., Xiao-qing, D., Sheng-jin, W., You-shou, W.: Background subtraction based on a combination of texture, color and intensity. In: Signal Processing, 2008. ICSP 2008. 9th International Conference on. pp. 1400-1405. IEEE (2008)
• Reference20 KaewTraKulPong, P., Bowden, R.: An improved adaptive background mixture model for real-time tracking with shadow detection. In: Video-based surveillance systems, pp. 135-144. Springer (2002)
• Reference21 Kristensen, F., Nilsson, P., Öwall, V.: Background segmentation beyond rgb. In: Asian COnference on Computer Vision. pp. 602-612. Springer (2006)
• Reference22 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)
• Reference23 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)
• Reference24 Pokrajac, D., Latecki, L.J.: Spatiotemporal blocks-based moving objects identication and tracking. IEEE Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS) pp. 70-77 (2003)
• Reference25 Power, P.W., Schoonees, J.A.: Understanding background mixture models for foreground segmentation. In: Proceedings image and vision computing New Zealand. vol. 2002 (2002)
• Reference26 Schindler, K., Wang, H.: Smooth foreground-background segmentation for video processing. In: Asian Conference on Computer Vision. pp. 581-590. Springer (2006)
• Reference27 Seki, M., Okuda, H., Hashimoto, M., Hirata, N.: Object modeling using gaussian mixture model for infrared image and its application to vehicle detection. Journal of Robotics and Mechatronics 18(6), 738 (2006)
• Reference28 Setiawan, N.A., Seok-Ju, H., Jang-Woon, K., Chil-Woo, L.: Gaussian mixture model in improved hls color space for human silhouette extraction. In: Advances in Artificial Reality and Tele-Existence, pp. 732-741. Springer (2006)
• Reference29 Shimada, A., Nonaka, Y., Nagahara, H., Taniguchi, R.i.: Case-based background modeling: associative background database towards low-cost and high-performance change detection. Machine vision and applications 25(5), 1121-1131 (2014)
• Reference30 Sigari, M.H., Mozayani, N., Pourreza, H.: Fuzzy running average and fuzzy background subtraction: concepts and application. International Journal of Computer Science and Network Security 8(2), 138-143 (2008)
• Reference31 Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: cvpr. p. 2246. IEEE (1999)
• Reference32 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)
• Reference33 Valentine, B., Apewokin, S., Wills, L., Wills, S.: An efficient, chromatic clusteringbased background model for embedded vision platforms. Computer Vision and Image Understanding 114(11), 1152-1163 (2010)
• Reference34 Varadarajan, S., Miller, P., Zhou, H.: Spatial mixture of gaussians for dynamic background modelling. In: Advanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference on. pp. 63-68. IEEE (2013)
• Reference35 White, B., Shah, M.: Automatically tuning background subtraction parameters using particle swarm optimization. In: Multimedia and Expo, 2007 IEEE International Conference on. pp. 1826-1829. IEEE (2007)
• Reference36 Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: Real-time tracking of the human body. IEEE Transactions on pattern analysis and machine intelligence 19(7), 780-785 (1997)
• Reference37 Yu, J., Zhou, X., Qian, F.: Object kinematic model: A novel approach of adaptive background mixture models for video segmentation. In: Intelligent Control and Automation (WCICA), 2010 8th World Congress on. pp. 6225-6228. IEEE (2010)
• Reference38 Zhang, H., Xu, D.: Fusing color and texture features for background model. In: Fuzzy Systems and Knowledge Discovery: Third International Conference, FSKD 2006, Xian, China, September 24-28, 2006. Proceedings 3. pp. 887-893. Springer (2006)
• Reference39 Zhao, Z., Bouwmans, T., Zhang, X., Fang, Y.: A fuzzy background modeling approach for motion detection in dynamic backgrounds. In: Multimedia and signal processing, pp. 177-185. Springer (2012)