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Dealing Background Issues in Object Detection using GMM: A Survey

by Lajari Alandkar, Sachin R. Gengaje
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 150 - Number 5
Year of Publication: 2016
Authors: Lajari Alandkar, Sachin R. Gengaje
10.5120/ijca2016911508

Lajari Alandkar, Sachin R. Gengaje . Dealing Background Issues in Object Detection using GMM: A Survey. International Journal of Computer Applications. 150, 5 ( Sep 2016), 50-55. DOI=10.5120/ijca2016911508

@article{ 10.5120/ijca2016911508,
author = { Lajari Alandkar, Sachin R. Gengaje },
title = { Dealing Background Issues in Object Detection using GMM: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 150 },
number = { 5 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 50-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume150/number5/26093-2016911508/ },
doi = { 10.5120/ijca2016911508 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:55:09.599592+05:30
%A Lajari Alandkar
%A Sachin R. Gengaje
%T Dealing Background Issues in Object Detection using GMM: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 150
%N 5
%P 50-55
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Moving object detection is critical task in video analytics. Gaussian Mixture Model (GMM) based background subtraction is widely popular technique for moving object detection due to its robustness to multimodality and lighting changes. This paper presents the critical survey about various GMM based approaches for handling critical background situations. This survey describes various challenges faced by background subtraction such as shadow, sudden and slow light changes, multimodal background, bootstrap, camouflage, foreground aperture, camera jitter etc. and study of various modifications or extensions of GMM to handle these issues. This study helps researcher to select appropriate GMM version based on critical background condition.

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. 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
  5. 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
  6. Wang H, Suter D. A Re-Evaluation of Mixture-of-Gaussian background modeling. 30th IEEE Int Conf on Acoustics, Speech, and Signal Processing (ICASSP 20 05 ), Pennsylvania, USA, March 2005, 1017-1020.
  7. Porikli F. Human body tracking by adaptive background models and mean-shift analysis. IEEE Int Workshop on Performance Evaluation of Tracking and Surveillance (PETS 2003), March 2003.
  8. Kristensen F, Nilsson P, Öwall V. Background segmentation beyond RGB. ACCV 2006, Hyderabad, Indian, 2006, 602 -612.
  9. Tian Y, Lu M, Hampapur A. Robust and efficient foreground analysis for real-time video surveillance. CVPR 2005, San Diego, USA, June 2005, 1182-1187.
  10. Al-Mazeed A, Nixon M, Gunn S. Classifiers combination for improved motion segmentation. Proc of Int Conf on Image Analysis and Recognition (ICIAR 2004), Porto, Portugal, 2004; 363-371.
  11. Teixeira L, Cardoso J, Corte-Real L. Object segmentation using background modelling and cascaded change detection. J Multimedia 2007; 2(5): 55-65.
  12. O. Javed, K. Shafique, and M. Shah, “A hierarchical approach to robust background subtraction using color and gradient information,” in Proceedings of IEEE Workshop on Motion and Video Computing (MOTION ’02), pp. 22–27, Orlando, Fla, USA, December 2002.
  13. Hu J, Su T. Robust background subtraction with shadow and highlight removal for indoor surveillance. J Adv Signal Proc 2007; 2007: 1-14.
  14. Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction. Int Conf Pattern Recognition (ICPR 2004), 2004, 2: 28-31.
  15. Lee D. Improved adaptive mixture learning for robust video background modeling. IAPR Workshop on Machine Vision for Application (MVA 2002), Nara, Japan, December 2002; 443-446
  16. Wang W, Gao W, Yang J, Chen D. Modeling background from compressed video. The Second Joint IEEE Int Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, in conjunction with the Tenth IEEE Int Conf on Computer Vision (ICCV 2005), Beijing, China, October 2005; 161-168.
  17. Zhao, Z., Bouwmans, T., Zhang, X. and Fang, Y., 2012. A fuzzy background modeling approach for motion detection in dynamic backgrounds. In Multimedia and Signal Processing (pp. 177-185). Springer Berlin Heidelberg.
  18. Fida El Baf, Thierry Bouwmans, Bertrand Vachon. Type-2 Fuzzy Mixture of Gaussians Model: Application to Background Modeling. G. Bebis et al. (Eds). ISVC 2008, Dec 2008, Las Vegas, United States. Springer-Verlag, LNCS 5358 (Part I.), pp.772-781, 2008.
  19. Rao N, Di H, Xu G. Joint correspondence and background modeling based on tree dynamic programming. Int Conf on Pattern Recognition (ICPR 2006), 2006, 425-428.
  20. Achkar F, Amer A. Hysteresis-based selective Gaussian mixture models for real-time background maintenance. SPIE Symposium on Electronic Imaging, Conf on Visual Communications and Image, Proc, San Jose, CA, USA, January 2007.
  21. Amintoosi M, Farbiz F, Fathy M, Analoui M, Mozayani N. QR decomposition-based algorithm for background subtraction. ICASSP 2007, April 2007, 1: 1093-1096.
  22. Harville M, Gordon G, Woodfill J. Foreground segmentation using adaptive mixture models in color and depth. Proc of the IEEE Workshop on Detection and Recognition of Events in Video, Vancouver, Canada, July 2001
  23. Cristani M, Bicego M, Murino V. Integrated Region- and Pixelbased approach to background modeling. Proc of IEEE Workshop on Motion and Video Computing (MOTION 2002), 2002; 3-8.
  24. Gordon G, Darrell T, Harville M, Woodfill J. Background estimation and removal based on range and color. Proc of the IEEE Conf on Computer Vision and Pattern Recognition (CVPR 1999), June 1999, 2: 459-464
  25. Utasi A, Czúni L. Reducing the foreground aperture problem in mixture of gaussians based motion detection. 6th EURASIP Conf Focused on Speech and Image Processing, Multimedia Communications and Services EC-SIPMCS 2007, Maribor, Slovenia, 2007.
  26. 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.
Index Terms

Computer Science
Information Sciences

Keywords

Object Detection Background Subtraction Gaussian Mixture Model Background challenges

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