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Foreground Detection in Surveillance Videos Via a Hybrid Local Texture Based Method Cover

Foreground Detection in Surveillance Videos Via a Hybrid Local Texture Based Method

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Open Access
|Dec 2016

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 2013.10.1109/TCSVT.2012.2203199
  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 2012.10.1007/s00138-011-0377-1
  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 2008.10.21307/ijssis-2017-308
  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, 2008.10.2174/2213275910801030219
  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.19371944, 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 2014.10.1016/j.cosrev.2014.04.001
  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 2006.10.1088/1742-6596/48/1/072
  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. 11331138, 2010.
  11. C. Silva, T. Bouwmans and C. Frélicot, “An eXtended Center-Symmetric Local Binary Pattern for Background Modeling and Subtraction in Videos”, 2014.10.5220/0005266303950402
  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, 2006.10.1007/11744078_8
  13. Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction”, Pattern Recognition, Vol. 2, pp. 28-31, August 2004.10.1109/ICPR.2004.1333992
  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, 2009.10.1016/j.patcog.2008.08.014
  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, 2011.10.1007/978-3-642-19282-1_23
  21. K. Kim, T.H. Chalidabhongse, D. Harwood and L. Davis, “Real-time foregroundbackground segmentation using codebook model”, Real-time imaging, Vol. 11, No. 3, pp. 172185, 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, 2012.10.1007/978-1-4615-0913-4_11
Language: English
Page range: 1668 - 1686
Submitted on: Jun 13, 2016
Accepted on: Oct 1, 2016
Published on: Dec 1, 2016
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2016 Xiaojing Du, Guofeng Qin, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.