Abstract
We envisage a new algorithm, to detect moving objects having dynamic and challenging background conditions, by applying low rank weightage and fuzzy aggregated multi-feature similarity method. Model level fuzzy aggregation measure driven background model maintenance is used to ensure more robustness. The model and current feature vectors are evaluated between corresponding elements to find out the similarity functions. To compute fuzzy similarities from the ordered similarity function values for each model concepts of Sugeno and Choquet integrals are incorporated in our algorithm. A fuzzy integral set is using model updating and foreground/background classification decision methods. Sugeno Integral calculates only minimum and maximum weightage. We use choquet concept because it has the same functionality as Sugeno but it also uses additional operations like arithmetic mean and Ordered Weighted Averaging (OWA). Here we explain to segment the object by fuzzy aggregation with low rank weightage concept for extracting moving objects with accurate shape in dynamic background. PSNR, MSE and SSIM values are calculated to do performance evaluation.
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References
Yilmaz, A., Javed, O., Shah, M.: Object Tracking: A Survey. ACM Computing Surveys 38(4), 1–45 (2006)
Moeslund, T., Hilton, A., Kruger, V.: A Survey of Advances in Vision-Based Human Motion Capture and Analysis, Computer Vision and Image Understanding. Computer Vision and Image Understanding 104(2/3), 90–126 (2006)
Chiu, C., Ku, M., Liang, L.: A robust object segmentation system using a probability-based background extraction algorithm. IEEE Trans. Circle Syst. Video 20(4), 518–528 (2010)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. Proc. IEEE Conf. CVPR 2, 246–252 (1999)
Tang, Z., Miao, Z.: Fast background subtraction and shadow elimination using improved Gaussian mixture model. In: Proc. IEEE Workshop Haptic, Audio, Visual Environ. Games, pp. 541–544 (2007)
Zhang, S., Yao, H., Liu, S.: Dynamic background modeling and subtraction using spatio-temporal local binary patterns. In: Proc. 15th IEEE ICIP, pp. 1556–1559 (2008)
Heikkila, H., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)
Wang, L., Pan, C.: Fast and effective background subtraction based on ELBP. In: Proc. ICASSP, pp. 1394–1397 (2010)
Chiranjeevi, P., Sengupta, S.: Moving object detection in the presence of dynamic backgrounds using intensity and textural features. Electron. Imag. 20(4), 043009-1–043009-11 (2011)
Zhang, S., Yao, H., Liu, S., Chen, X., Gao, W.: A covariance-based method for dynamic background subtraction. In: Proc. ICPR, pp. 1–4 (2008)
Chiranjeevi, P., Sengupta, S.: Spatially correlated background subtraction, based on adaptive background maintenance. J. Vis. Commun. Image Represent. 23(6), 948–957 (2012)
Jabri, S., Duric, Z., Wechsler, H., Rosenfeld, A.: Detection and location of people using adaptive fusion of color and edge information. In: Proc. 15th ICPR, vol. 4, pp. 627–630 (2000)
Chen, Y., Chen, C., Huang, C., Hung, Y.: Efficient hierarchical method for background subtraction. Pattern Recognit 40(10), 2706–2715 (2007)
Wan, Q., Wang, Y.: Background subtraction based on adaptive nonparametric model. In: Proc. 7th WCICA, pp. 5960–5965 (2008)
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Gayathri, A., Srinivasan, A. (2015). Moving Object Detection by Fuzzy Aggregation Using Low Rank Weightage Representation. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_36
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DOI: https://doi.org/10.1007/978-3-319-11933-5_36
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