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


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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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

C. Stauffer and, W. E. L. Grimson, “Adaptive background mixture models for real-time tracking”, Computer Vision and Pattern Recognition, Vol. 2, 1999. Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

Z. Zivkovic, “Improved adaptive Gaussian mixture model for background subtraction”, Pattern Recognition, Vol. 2, pp. 28-31, August 2004.10.1109/ICPR.2004.1333992 Search in Google Scholar

B. White and M. Shah, “Automatically tuning background subtraction parameters using particle swarm optimization”, Multimedia and Expo, pp. 1826-1829), July, 2007. Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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 Search in Google Scholar

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. Search in Google Scholar

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 Search in Google Scholar

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Engineering, Introductions and Overviews, other