Comprehensive Study and Comparative Analysis of Different Types of Background Sub-traction Algorithms

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Author(s)

Priyank Shah 1,* Hardik Modi 1

1. Charotar University of Science and technology, Gujarat, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2014.08.07

Received: 20 Mar. 2014 / Revised: 25 Apr. 2014 / Accepted: 4 Jun. 2014 / Published: 8 Jul. 2014

Index Terms

Background Subtraction, Moving Object Detection, Video Surveillance, Mean Filtering, Median Filtering, W4 System, Frame Differencing, Running Gaussian Average, Gaussian Mixture Model, Eigen Background

Abstract

There are many methods proposed for Back-ground Subtraction algorithm in past years. Background subtraction algorithm is widely used for real time moving object detection in video surveillance system. In this paper we have studied and implemented different types of meth-ods used for segmentation in Background subtraction algo-rithm with static camera. This paper gives good under-standing about procedure to obtain foreground using exist-ing common methods of Background Subtraction, their complexity, utility and also provide basics which will useful to improve performance in the future . First, we have explained the basic steps and procedure used in vision based moving object detection. Then, we have debriefed the common methods of background subtraction like Sim-ple method, statistical methods like Mean and Median filter, Frame Differencing and W4 System method, Running Gaussian Average and Gaussian Mixture Model and last is Eigenbackground Model. After that we have implemented all the above techniques on MATLAB software and show some experimental results for the same and compare them in terms of speed and complexity criteria.

Cite This Paper

Priyank Shah, Hardik Modi,"Comprehensive Study and Comparative Analysis of Different Types of Background Sub-traction Algorithms", IJIGSP, vol.6, no.8, pp.47-52, 2014. DOI: 10.5815/ijigsp.2014.08.07

Reference

[1]A. M. McIvor," Background subtraction techniques, Proc. of Image and Vision Computing,pp.155-163",2000.

[2]J Nascimento and J Marques, "Performance evaluation of object detection algorithms for video surveillance", IEEE Transactions on Multimedia , Vol- 8 , Issue-4,pp-761-774,Aug 2006.

[3]K Gupta1, A Kulkarni,"Implementation of an Automated Single Camera Object Tracking System Using Frame Dif-ferencing, and Dynamic Template Matching", CISSE 07 Co-Sponsored by IEEE,pp 245-250, Dec 2007.

[4]K Jadav, M .Lokhandwala, A Gharge," Vision based moving object detection and tracking",National Conference on Recent Trends in Engineering & Technology,pp.13-14, May 2011.

[5]M Piccardi,"Background subtraction techniques: a review", IEEE International Conference on Systems, Man and Cyber-netics, Vol-4, pp 3099-3104, 2004.

[6]X Song, J Chen, X Zhou,"A Robust Moving Objects Detec-tion Based on Improved Gaussian Mixture Model", IEEE In-ternational Conference on Artificial Intelligence and Compu-tational Intelligence, Vol.2, pp 54-58, 2010.

[7]Z Bian and X Dong,"Moving Object Detection Based on Improved Gaussian Mixture Model", 5th International Con-gress on Image and Signal Processing, pp 109-112,2012.

[8]C. Stauffer and W Grimson, “Adaptive background mixture models for real-time tracking,”, IEEE CVPR, Vol.2, June 1999.

[9]C. Wren, A. Azarhayejani, T. Darrell, and A.P. Pentland, “Pfinder: real-time tracking of the human body”, IEEE Trans. on Patfern Anal. and Machine Infell., vol. 19, Issue. 7, pp. 780-785, 1997.

[10]N Oliver, B Rosario, and A Pentland, “A Bayesian computer vision system for modeling human interactions”, IEEE Trans. on Paftern Anal. and Machine Zntell., vol. 22, Issue. 8, pp. 831-843, 2000.

[11]C Zhang ,A Pan, S Zheng, X Cao,"Motion Object Detection Of Video Based On Principal Component Analysis", IEEE International Conference on Machine Learning and Cyber-netics, Vol-5,pp 3938-3943, 2008.

[12]K Joshi, D Thakore ,"A Survey on Moving Object Detection and Tracking in Video Surveillance System", , International Journal of Soft Computing and Engineering, Vol-2, Issue-3, pp 44-48, July 2012.

[13]I Haritaoglu, D Harwood, and L Davis, “W4: real-time surveillance of people and their activities”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, August 2000.

[14]D Maniciu, P Meer "Mean shift: A robust approach toward feature space analysis", IEEE Trans. Patt. Analy. Mach. Intell. Vol-24, Issue- 5, pp 603–619, ,August 2002.

[15]B Moore," Principal component analysis in linear systems: Controllability, observability, and model reduction",IEEE Transactions on Automatic Control, Volume:26 , Issue: 1, PP-17-32, 06 January 2003.

[16]H Hotelling , "Simplified calculation of principal components", Psychometrica, vol. 1, pp.27 -35,1936.

[17]A. Elgammal, D Hanvood, L Davis, “Non- parametric model for background subtraction”, Proc. ECCV 2000, pp. 751-767, June 2000.

[18]C Wang, L Lan, Y Zhang, M Gu, "Face Recognition Based on Principle ComponentAnalysis and Support Vector Machine", 3rd IEEE International workshop on Intel. System and Application, pp 1-4,2011.

[19]H Zhipeng, Y Wang, Y Tian, T Huang"Selective eigenbackgrounds method for background subtraction in crowed scenes",18th IEEE International Conference on Image Processing,Issue-1522-4880,pp3277-3280,2011.

[20]M. Seki, T Wada, H Fujiwara, K Sumi, “Background subtraction based on cooccurrence of image variations”, Proc. CVPR 2003, Vol. 2, pp. 65-72, 2003.

[21]http://www.cs.utexas.edu/~grauman/courses/fall2009/slides/lecture9_background.pdf.

[22]http://imagelab.ing.unimore.it/visor/video_details.asp?idvideo=45.