FOREGROUND DETECTION IN SURVEILLANCE VIDEOS VIA A HYBRID LOCAL TEXTURE BASED METHOD

Publications

Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

GET ALERTS

eISSN: 1178-5608

DESCRIPTION

7
Reader(s)
25
Visit(s)
0
Comment(s)
0
Share(s)

VOLUME 9 , ISSUE 4 (December 2016) > List of articles

FOREGROUND DETECTION IN SURVEILLANCE VIDEOS VIA A HYBRID LOCAL TEXTURE BASED METHOD

Xiaojing Du / Guofeng Qin

Keywords : Foreground detection, background modeling, derivations of local binary pattern.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 9, Issue 4, Pages 1,668-1,686, DOI: https://doi.org/10.21307/ijssis-2017-934

License : (CC BY-NC-ND 4.0)

Received Date : 13-June-2016 / Accepted: 01-October-2016 / Published Online: 01-December-2016

ARTICLE

ABSTRACT

Foreground detection is a basic but challenging task in computer vision. In this paper, a
novel hybrid local texture based method is presented to model the background for complex scenarios
and an image segmentation based denoising processing is applied to reduce noise. We combine the
uniform pattern of eXtended Center-Symmetric Local Binary Pattern (XCS-LBP) and Center-
Symmetric Local Derivative Pattern (CS-LDP) to generate a discriminative feature with shorter
histogram. Retaining the strengths of the two textures, it appears to be robust to dynamic scenes,
illumination changes and noise. Based on the hybrid feature, we employ an overlapping block based
Gaussian Mixture Model (GMM) framework which makes classifying decision in pixel level.
Experimental results on two changeling datasets (Wallflower and I2R dataset) clearly justify the
performance of proposed method. Besides, we take the foreground masks obtained by proposed method
as input to a tracking system showing notable results.

Content not available PDF Share

FIGURES & TABLES

REFERENCES

[1] V. Reddy, C. Sanderson and B.C. Lovell, “Improved foreground detection via block-based
classifier cascade with probabilistic decision integration”, IEEE Transactions on Circuits and
Systems for Video Technology, Vol. 23, pp. 83-93, January 2013.
[2] E.A.J. Abadi, S.A. Amiri, M. Goharimanesh and A. Akbari, “Vehicle model recognition
based on using image processing and wavelet analysis”, International Journal on Smart Sensing
and Intelligent Systems, Vol. 8, No.4, pp. 2212-2230, December 2015.
[3] Y.L. Tian, A. Senior and M. Lu, “Robust and efficient foreground analysis in complex
surveillance videos”, Machine Vision and Applications, Vol. 23, pp. 967-983, September 2012.
[4] S.H Kim, K. Sekiyama, T. Fukuda, “Pattern Adaptive and Finger Image-guided Keypad
Interface for In-vehicle Information Systems”, International Journal on Smart Sensing and
Intelligent Systems, Vol. 1, No. 3, pp. 572-591, September 2008.
[5] T. Bouwmans, F.E. Baf, and B. Vachon, “Background modeling using mixture of gaussians
for foreground detection: a survey”, Recent Patents on Computer Science, Vol. 1, pp. 219-237,
2008.
[6] S. Brutzer, B. Hoferlin, and G. Heidemann, “Evaluation of background subtraction techniques
for video surveillance”, Computer Vision and Pattern Recognition (CVPR), Vol.32, pp.1937-
1944, 2011.
[7] T. Bouwmans, “Traditional and recent approaches in background modeling for foreground
detection: an overview”, Computer Science Review, Vol. 11, pp. 31–66, May 2014.
[8] C. Stauffer and, W. E. L. Grimson, “Adaptive background mixture models for real-time
tracking”, Computer Vision and Pattern Recognition, Vol. 2, 1999.
[9] X. H. Fang, W. Xiong, B. J. Hu and L. T, Wang, “A moving object detection algorithm based
on color information”, Journal of Physics: Conference Series, Vol. 48, pp. 384, October 2006.
[10] H. Bhaskar, L. Mihaylova and A. Achim, “Video foreground detection based on symmetric
alpha-stable mixture models”, Circuits and Systems for Video Technology, Vol. 20, pp. 1133-
1138, 2010.
[11] C. Silva, T. Bouwmans and C. Frélicot, “An eXtended Center-Symmetric Local Binary
Pattern for Background Modeling and Subtraction in Videos”, 2014.
[12] K. Kim and L.S. Davis, “Multi-camera tracking and segmentation of occluded people on
ground plane using search-guided particle filtering”, pp. 98-109, 2006.
[13] Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction”,
Pattern Recognition, Vol. 2, pp. 28-31, August 2004.
[14] B. White and M. Shah, “Automatically tuning background subtraction parameters using
particle swarm optimization”, Multimedia and Expo, pp. 1826-1829), July, 2007.
[15] M. Mason and Z. Duric, “Using histograms to detect and track objects in color video”,
Applied Imagery Pattern Recognition Workshop, pp. 154-159, October, 2001
[16] T. Ojala, M. Pietikainen and D. Harwood, “Performance evaluation of texture measures with
classification based on Kullback discrimination of distributions”, Pattern Recognition, Vol. 1, No.
1, pp. 582-585, November, 1994.
[17] G. Xue, L. Song, J. Sun and M. Wu, “Hybrid center-symmetric local pattern for dynamic
background subtraction”, Multimedia and Expo (ICME), pp. 1-6, July, 2011.
[18] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, “SLIC superpixels
compared to state-of-the-art superpixel methods”, Pattern Analysis and Machine Intelligence, Vol.
34, No. 11, pp. 2274-2282, 2012.
[19] M. Heikkilä, M. Pietikäinen and C. Schmid, “Description of interest regions with local
binary patterns”, Pattern recognition, Vol. 42, No. 3, pp. 425-436, 2009.
[20] Y. Zheng, C. Shen, R. Hartley and X. Huang, “Pyramid center-symmetric local
binary/trinary patterns for effective pedestrian detection”, ACCV, pp. 281-292, 2011.
[21] K. Kim, T.H. Chalidabhongse, D. Harwood and L. Davis, “Real-time foreground–
background segmentation using codebook model”, Real-time imaging, Vol. 11, No. 3, pp. 172-
185, 2005.
[22] P. KaewTraKulPong and R. Bowden, “An improved adaptive background mixture model for
real-time tracking with shadow detection”, Video-based surveillance systems, pp. 135-144, 2012.

EXTRA FILES

COMMENTS