Adaptive Background Image Calculation for Video Sequences Based on Optical Flow

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Abstract:

A new method is proposed to calculate the background in video sequences. The optical flow is estimated to determine the local regions occupied by moving objects. The background image is calculated by an efficient averaging process excluding the moving object regions, which overcomes the foreground-occluding problem in direct averaging method for background estimation. The experiments for traffic video processing prove the method’s effectiveness and robustness.

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Advanced Materials Research (Volumes 945-949)

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1820-1824

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June 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Fatih Porikli, Franuois Brémond, Shiloh Dockstader, et. al., IEEE Signal Processing Magazine, Vol. 30, No. 3 (2013), pp.190-198.

Google Scholar

[2] Cheng-Hung Chuang, Zhen-You Lian, ICIC Express Letters, Vol. 8, No. 4 (2014), pp.1111-1118.

Google Scholar

[3] Kenji Watanabe, Michikazu Umemura, Masakatsu Higashikubo, SEI Technical Review, No. 76 (2013), pp.90-93.

Google Scholar

[4] Marco Cristani, R. Raghavendra, Alessio Del Bue, Murino Vittorio, Neurocomputing, Vol. 100 (2013), pp.86-97.

DOI: 10.1016/j.neucom.2011.12.038

Google Scholar

[5] Stephen J. McKenna, Sumer Jabri, Zoran Duric, et. al., Computer Vision & Image Understanding, Vol. 80, No. 1 (2000), pp.42-56.

DOI: 10.1006/cviu.2000.0870

Google Scholar

[6] Manuel Vargas, Jose Manuel Milla, Sergio L. Toral, Federico Barrero, IEEE Transactions on Vehicular Technology, Vol. 59, No. 8 (2010), pp.3694-3709.

DOI: 10.1109/tvt.2010.2058134

Google Scholar

[7] Information on http: /www. cse. msu. edu/~gutchess/traffic, Dan Gutchess, Motion Estimation of Traffic.

Google Scholar

[8] Richard P. Wildes, Michael J. Amabile, Ann-Marie Lanzillotto, Tzong-Shyng Leu, Computer Vision & Image Understanding, Vol. 80 No. 2 (2000), pp.246-266.

DOI: 10.1006/cviu.2000.0874

Google Scholar

[9] Bineng Zhong, Yan Chen, Yingju Shen, et. al., Neurocomputing, Vol. 123 (2014), pp.344-353.

Google Scholar

[10] Yi Tang, Weining Liu, Journal of Information and Computational Science, Vol. 10, No. 14 (2013) , pp.4593-4601.

Google Scholar

[11] Hamidreza Baradaran Kashani, Hadi Sadoghi Yazdi, Seyed Alireza Seyedin, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 25, No. 1 (2011), pp.1-35.

Google Scholar

[12] Berthold K. P. Horn, Brian G. Schrunck: Determining Optical Flow, A. I. Memo No. 572, Massachusetts Institute of Technology, (1980).

Google Scholar

[13] Martin Mueller, Peter Karasev, Ivan Kolesov, Allen Tannenbaum, IEEE Transactions on Image Processing, Vol. 22, No. 7 (2013), pp.2786-2797.

DOI: 10.1109/tip.2013.2258353

Google Scholar

[14] Mikhail G. Mozerov, IEEE Transactions on Image Processing, Vol. 22, No. 5 (2013), p.2044-(2055).

Google Scholar