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http://dx.doi.org/10.5762/KAIS.2016.17.1.717

Change Area Detection using Color and Edge Gradient Covariance Features  

Kim, Dong-Keun (Division of Computer Science and Engineering, Kongju National University)
Hwang, Chi-Jung (Dept. of Computer Engineering, Chungnam National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.17, no.1, 2016 , pp. 717-724 More about this Journal
Abstract
This paper proposes a change detection method based on the covariance matrices of color and edge gradient in a color video. The YCbCr color format was used instead of RGB. The color covariance matrix was calculated from the CbCr-channels and the edge gradient covariance matrix was calculated from the Y-channels. The covariance matrices were effectively calculated at each pixel by calculating the sum, squared sum, and sum of two values' multiplication of a rectangle area using the integral images from a background image. The background image was updated by a running the average between the background image and a current frame. The change areas in a current frame image against the background were detected using the Mahalanobis distance, which is a measure of the statistical distance using covariance matrices. The experimental results of an expressway color video showed that the proposed approach can effectively detect change regions for color and edge gradients against the background.
Keywords
Change Detection; YCbCr color; Covariance; Integral images; Video;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Richard J. Radke, et., al., "Image Change Detection Algorithms: A Systematic Survey." IEEE Trans. on Image processing, vol.14, no.3(2005), pp.294-307. DOI: http://dx.doi.org/10.1109/TIP.2004.838698   DOI
2 Alan M. McIvor, "Background Subtraction Techniques," In Processings of Image & Vision Computing New Zealand, IVCNZ, 2000.
3 Massimo Piccardi, "Background subtraction techniques: a review," IEEE International Conference on Systems, Man and Cybernetics, pp.3099-3104, 2004. DOI: http://dx.doi.org/10.1109/icsmc.2004.1400815   DOI
4 C. Stauffer and E. Grimson, "Adaptive background mixture models for real-time tracking", CVPR, vol.2, pp. 246-252, 1998.
5 Kevin Mader and Gil Reese, "Using Covariance Matrices as Feature Descriptors for Vehicle Detection from a Fixed Camera," Digital Image Processing and Communication, http://arxiv.org/pdf/1202.2528.pdf, Boston University, 2006.
6 O. Tuzel 2006] O. Tuzel, F. Porikli, P. M. "Region covariance: A fast descriptor for detection and classication," ECCV, 2006
7 Fatih Porikli and Oncel Tuzel "FAST CONSTRUCTION OF COVARIANCE MATRICES FOR ARBITRARY SIZE IMAGE," WINDOWS
8 W. Forstner and W. Forstner, B. M. "A metric for covariance matrices. TR, Dept. of Geodesy and Geoinfromatics, Stuttgart University, 1999.
9 F. Crow, "Summed-area tables for texture mapping", Proceedings of SIGGRAPH, 1984. DOI: http://dx.doi.org/10.1145/800031.808600   DOI
10 Paul Viola and Michael Jones, "Rapid object detection using a boosted cascade of simple features" CVPR, 2001.
11 F. Porikli, "Integral Histogram: A fast way to extract histograms in Cartesian spaces", in Proceedings of CVPR. 2005. DOI: http://dx.doi.org/10.1109/cvpr.2005.188
12 Geum-boon Lee and Beom-joon Cho, "A Novel Method for Moving Object Tracking using Covariance Matrix and Riemannian Metric," Journal of KIMICS vol.15, no.2, pp.364-370, 2011.
13 Geum-boon Lee, "A Fast Moving Object Tracking Method by the Combination of Covariance Matrix and Kalman Filter Algorithm," JKIICE, vol.19, no.6, pp.1477-1484, 2015.
14 Dongkeun Kim, "Change Detection using Integral Images in Color Video Sequences," Information vol. 18, pp.229-235, 2015.
15 Geoffrey John McLachlan, "Mahalanobis Distance," RESONANCE, 1999, pp20-26.
16 R. De Maesschalck, D. Jouan-Rimbaud, D.L. Massart, "The Mahalanobis distance," Chemometrics and Intelligent Laboratory Systems, vol. 50, pp. 1-18, 2000. DOI: http://dx.doi.org/10.1016/S0169-7439(99)00047-7   DOI
17 DongKeun Kim, "Moving Object Detection using Gaussian Pyramid based Subtraction Images in Road Video Sequences," Journal of the Korea Academia-Industrial Cooperation Society, vol.12, no.12, pp.5856-5864. 2011. DOI: http://dx.doi.org/10.5762/KAIS.2011.12.12.5856   DOI