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Reseach Article

Pixel’s History based Background Subtraction Parameters Tuning using Fuzzy Inference System

by Lajari Alandkar, Sachin R. Gengaje
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 148 - Number 9
Year of Publication: 2016
Authors: Lajari Alandkar, Sachin R. Gengaje
10.5120/ijca2016911308

Lajari Alandkar, Sachin R. Gengaje . Pixel’s History based Background Subtraction Parameters Tuning using Fuzzy Inference System. International Journal of Computer Applications. 148, 9 ( Aug 2016), 23-29. DOI=10.5120/ijca2016911308

@article{ 10.5120/ijca2016911308,
author = { Lajari Alandkar, Sachin R. Gengaje },
title = { Pixel’s History based Background Subtraction Parameters Tuning using Fuzzy Inference System },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 148 },
number = { 9 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 23-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume148/number9/25786-2016911308/ },
doi = { 10.5120/ijca2016911308 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:52:54.640546+05:30
%A Lajari Alandkar
%A Sachin R. Gengaje
%T Pixel’s History based Background Subtraction Parameters Tuning using Fuzzy Inference System
%J International Journal of Computer Applications
%@ 0975-8887
%V 148
%N 9
%P 23-29
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Object detection is one of the challenging steps in video surveillance. The most popular and robust technique for object detection is background subtraction. It is always challenging to obtain better performance of background subtraction algorithm as it requires appropriate initial tuning of common parameters like number of components in Gaussian Mixture Model (GMM), threshold, learning rate and initial values. Traditional way of tuning is manual selection of parameters based on background scenario. It requires good understanding of background scene to the end user and iterative experimentation with manual setting leading to significantly time intensive and tedious tuning process. As initial tuning affects performance of background subtraction, it makes significant impact on usage of an algorithm and its selection based on current application. In this paper, simplified novel methodology of pixel’s history based parameter tuning is proposed. Method uses statistical features to approximate background situation and fuzzy logic approach to bound tuning criteria. Broadly, statistical features are extracted from pixel’s history and processed by Fuzzy Inference System (FIS). GMM parameters as FIS output are exclusively used for background subtraction. Algorithm evidently demonstrates its effectiveness in parameter selection. The proficiency of proposed tuning system is also highlighted by comparison with manual and Particle Swarm Optimization (PSO) based method over diverse Wallflower Dataset.

References
  1. Jun-Wei Hsieh, Shih-Hao Yu, Yung-Sheng Chen, An Automatic Traffic Surveillance System for Vehicle Tracking and Classification, IEEE Transactions on Intelligent Transportation Systems, Vol. 7,
  2. S. Kannan, A. Sivasankar, Moving Object Detection Based on Fuzzy Color Histogram Features and Dynamic Threshold Optimization , The International Journal of Science & Technoledge, Vol 2 Issue 3 March, 2014
  3. Thierry Bouwmans, Fida El Baf, Bertrand Vachon. “Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey”. Recent Patents on Computer Science, Bentham Science Publishers, 2008, 1 (3), pp.219-237.
  4. Lucia Maddalena, Alfredo Petrosino, “A fuzzy spatial coherence-based approach to background/ foreground separation for moving object detection”, Neural Comput & Applic,2010
  5. Yannick Benezeth, Pierre-Marc Jodoin, Bruno Emile, Helene Laurent, Christophe Rosenberger, “Comparative study of background subtraction algorithms”. Journal of Electronic Imaging, Society of Photo-optical Instrumentation Engineers, 2010
  6. Qi Zang and Reinhard Klette, “Parameter Analysis for Mixture of Gaussians Model”, www.citr.auckland.ac.nz/researchreports/CITR-TR-188.pdf
  7. Brandyn White B, Shah M. Automatically tuning background subtraction parameters using particle swarm optimization. IEEE Int Conf on Multimedia & Expo (ICME 2007), Beijing, China, 2007; 1826-1829.
  8. Stauffer C, Grimson W. “Adaptive background mixture models for real-time tracking”. Proc. IEEE Conf. on Comp Vision and Patt. Recog.(CVPR 1999) 1999; 246-252
  9. Zivkovic Z. “Improved adaptive Gaussian mixture model for background subtraction”. Int Conf Pattern Recognition (ICPR 2004), 2004, 2: 28-31.
  10. Cheng J, Yang J, Zhou Y, Cui Y. Flexible background mixture models for foreground segmentation. J Image Vision Comput (IVC 2006) 2006; 24: 473-482.
  11. Shimada A, Arita D, Taniguchi R. Dynamic control of adaptive mixture-of-gaussians background model. AVSS 2006, Sydney, Australia, November 2006, 5.
  12. Tan R, Huo H, Qian J, Fang T. Traffic video segmentation using adaptive-k gaussian mixture model. The Int Workshop on Intelligent Computing (IWICPAS 2006), Xi'An, China, August 2006, 125-134.
  13. Carminati L, Benois-Pinau J. Gaussian mixture classification for moving object detection in video surveillance environment. IEEE Int Conf on Image Processing (ICIP 2005), September 2005, 113- 116.
  14. KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow detection. Proceedings 2nd European Workshop on Advanced Video Based Surveillance Systems (AVBS 2001) , Kingston, UK, September 2001.
  15. KaewTraKulPong P, Bowden R. Adaptive visual system for tracking low resolution color targets. BMVC 2001 , Manchester UK, September 2001, 1: 243-252.
  16. KaewTraKulPong P, Bowden R. A real-time adaptive visual surveillance system for tracking low resolution color targets in dynamically changing scenes. J Image Vision Comput 2003; 21(10): 913-929
  17. Haque M, Murshed M, Paul M. On stable dynamic background generation technique using Gaussian Mixture models for robust object detection. 5th IEEE Int Conf On Advanced Video and Signal Based Surveillance, AVSS 2008, 2008
  18. Haque M, Murshed M, Paul M. A hybrid object detection technique from dynamic background using gaussian mixture models. IEEE Int Workshop on Multimedia Signal Processing, MMSP 2008, Cairns, Queensland, Australia, October 2008.
  19. Haque M, Murshed M, Paul M. Improved Gaussian Mixtures for robust object detection by adaptive multi-background generation. Int Con on Pattern Recognition, ICPR 2008, Tampa, Florida, USA, December 2008.
  20. Lee D. Online adaptive Gaussian mixture learning for video applications. Workshop on Statistical Methods for Video Processing (SMVP 2004), Prague, Czech, May 2004; 105-116.
  21. Morellas V, Pavlidis I, Tsiamyrtzis P. DETER: Detection of events for threat evaluation and recognition. Mach Vision Appl 2003; 15: 29-45.
  22. Zhang Y, Liang Z, Hou Z, Wang H, Tan M. An adaptive mixture gaussian background model with online background reconstruction and adjustable foreground mergence time for motion segmentation. ICIT 2005, December 2005; 23-27.
  23. Amintoosi M, Farbiz F, Fathy M, Analoui M, Mozayani N. QR decomposition-based algorithm for background subtraction. ICASSP 2007, April 2007, 1: 1093-1096.
  24. Swati Chaudhari, Manoj Patil, “Study and Review of Fuzzy Inference Systems for Decision Making and Control”, American International Journal of Research in Science, Technology, Engineering & Mathematic, issue 5, Feb 2014, pp. 88-92
  25. Wallflower Dataset: http://research.microsoft.com/users/jckrumm/WallFlower/TestImages.htm
  26. Toyama K, Krumm J, Brumitt B, Meyers B. Wallflower: Principles and practice of background maintenance. Int. Conf. on Computer Vision, (ICCV 1999), Corfu, Greece, September 1999; 255 -261
  27. Fundamentals of Logic Concepts (2011). Retrieved from http://ptgmedia.pearsoncmg.com/images/0135705991/samplechapter/0135705991.pdf
Index Terms

Computer Science
Information Sciences

Keywords

Object Detection Background Subtraction Gaussian Mixture Model Fuzzy Inference System.