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To overcome the limitation of existing algorithms for detecting moving objects from the dynamic scenes, a foreground detection algorithm based on optical flow field analysis is proposed. Firstly, the object boundary information is determined by detecting the differences in optical flow gradient magnitude and optical flow vector direction between foreground and background. Then, the pixels inside the objects are obtained based on the point-in-polygon problem from computational geometry. Finally, the superpixels per frame are acquired by over-segmenting method. And taking the superpixels as nodes, the Markov Random field model is built, in which the appearance information fitted by Gaussian Mixture Model is combined with spatiotemporal constraints of each superpixel. The final foreground detection result is obtained by finding the minimum value of the energy function. The proposed algorithm does not need any priori assumptions, and can effectively realize the moving object detection in dynamic and stationary background. The experimental results show that the proposed algorithm is superior to the existing state-of-the-art algorithms in the detection accuracy, robustness and time consuming.
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Keywords:
- dynamic scene /
- moving object detection /
- optical flow field analysis /
- Markov random field model
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[17] Adhyapak S, Kehtarnavaz N, Nadin M 2007 J. Electron. Imaging 16 13012
[18] Di S, Mattoccia S, Tombari F 2005 International Workshop on Computer Architecture for Machine Perception Palermo, Italy, July 4-6, 2005 p193
[19] Bouguet J 2001 Intel Corporation 5 10
[20] Brox T, Malik J 2010 European Conference on Computer Vision (ECCV) Crete, Greece, September 5-11, 2010 p282
[21] Achanta R, Shaji A, Smith K 2012 IEEE Trans. Pattern Anal. Mach. Intell. 34 2274
[22] Achanta R, Shaji A 2010 EPFL Technical Report 1 149
[23] Vazquez A, Avidan S, Pfister H 2010 European Conference on Computer Vision (ECCV) Crete, Greece, September 5-11, 2010 p268
[24] Fulkerson B, Vedaldi A, Soatto S 2009 International Conference on Computer Vision (ICCV) Kyoto, Japan, September 27-October 4, 2009 p670
[25] Boykov Y, Veksler O, Zabih R 2001 IEEE Trans. Pattern Anal. Mach. Intell. 23 1222
[26] Boykov Y, Funka L 2006 Int. J. Comput. Vison. 70 109
[27] Tron R, Vidal R 2007 Conference on Computer Vision and Pattern Recognition (CVPR) Minneapolis, USA, June 18-23 2007 p1
[28] Sand P, Teller S 2008 Int. J. Comput. Vison. 80 72
[29] Goyette N, Jodoin P, Porikil F 2012 Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Providence, Rhode Island, June 16-21, 2012 p1
[30] Cui X, Huang J, Zhang S, Metaxas D 2012 European Conference on Computer Vision (ECCV) Florence, Italy, October 7-13, 2012 p612
[31] Sundaram N, Brox T, Keutzer K 2010 European Conference on Computer Vision (ECCV) Crete, Greece, September 5-11, 2010 p438
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[1] Radke R, Andra S, Kofahi A, Roysam B 2005 IEEE Trans. Image Process. 14 294
[2] Ren Y, Chua C, Ho Y 2003 Mach. Vision Appl. 13 332
[3] Sheikh Y, Javed O, Kanade T 2009 Conference. on Computer Vision and Pattern Recognition(CVPR) Miami, USA, June 20-25, 2009 p1219
[4] Chen L, Zhu S, Li X 2015 International Symposium on Computers Informatics Beijing, China, January 17-18, 2015 p742
[5] Bi G L, Xu Z J, Chen T, Wang J L, Zhang Y S 2015 Acta Phys. Sin. 64 150701 (in Chinese) [毕国玲, 续志军, 陈涛, 王建立, 张延坤 2015 物理学报 64 150701]
[6] Sun S W, Wang Y F, Huang F, Liao H Y 2013 J. Visual. Commun. Image Represent 24 232
[7] Li A H, Cui Z G 2016 Moving Object Detection in Videos (Beijing: Science Press) p15 (in Chinese) [李艾华, 崔智高 2016 视频序列运动目标检测技术 (北京: 科学出版社) 第15页]
[8] Lee Y, Kim J, Grauman K 2011 International Conference on Computer Vision(ICCV) Barcelona, Spain, November 6-13, 2011 p1995
[9] Li W T, Chang H S, Lien K C, Chang H T, Wang Y C 2011 IEEE Trans. Image Proc. 22 2600
[10] Zhang D, Javed O, Shah M 2013 Conference on Computer Vision and Pattern Recognition(CVPR) Oregon, Portland, June 25-27, 2013 p682
[11] Elqursh A, Elgammal A 2012 European Conference on Computer Vision (ECCV) Florence, Italy, October 7-13, 2012 p228
[12] Gao W, Tang Y, Zhu M 2014 Acta Phys Sin. 63 094204 (in Chinese) [高文, 汤洋, 朱明 2014 物理学报 63 094204]
[13] Cui Z G, Li A H, Feng G Y 2015 Journal of Computer-Aided Design Computer Graphics 27 621 (in Chinese) [崔智高, 李艾华, 冯国彦 2015 计算机辅助设计与图形学学报 27 621]
[14] Wang J, Adelson E 1994 IEEE Trans. Image Process. 3 625
[15] Cremers D, Soatto S 2004 Int. J. Comput Vison 62 249
[16] Yoon S, Park S, Kang S 2005 Pattern Recognit. Lett. 26 2221
[17] Adhyapak S, Kehtarnavaz N, Nadin M 2007 J. Electron. Imaging 16 13012
[18] Di S, Mattoccia S, Tombari F 2005 International Workshop on Computer Architecture for Machine Perception Palermo, Italy, July 4-6, 2005 p193
[19] Bouguet J 2001 Intel Corporation 5 10
[20] Brox T, Malik J 2010 European Conference on Computer Vision (ECCV) Crete, Greece, September 5-11, 2010 p282
[21] Achanta R, Shaji A, Smith K 2012 IEEE Trans. Pattern Anal. Mach. Intell. 34 2274
[22] Achanta R, Shaji A 2010 EPFL Technical Report 1 149
[23] Vazquez A, Avidan S, Pfister H 2010 European Conference on Computer Vision (ECCV) Crete, Greece, September 5-11, 2010 p268
[24] Fulkerson B, Vedaldi A, Soatto S 2009 International Conference on Computer Vision (ICCV) Kyoto, Japan, September 27-October 4, 2009 p670
[25] Boykov Y, Veksler O, Zabih R 2001 IEEE Trans. Pattern Anal. Mach. Intell. 23 1222
[26] Boykov Y, Funka L 2006 Int. J. Comput. Vison. 70 109
[27] Tron R, Vidal R 2007 Conference on Computer Vision and Pattern Recognition (CVPR) Minneapolis, USA, June 18-23 2007 p1
[28] Sand P, Teller S 2008 Int. J. Comput. Vison. 80 72
[29] Goyette N, Jodoin P, Porikil F 2012 Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) Providence, Rhode Island, June 16-21, 2012 p1
[30] Cui X, Huang J, Zhang S, Metaxas D 2012 European Conference on Computer Vision (ECCV) Florence, Italy, October 7-13, 2012 p612
[31] Sundaram N, Brox T, Keutzer K 2010 European Conference on Computer Vision (ECCV) Crete, Greece, September 5-11, 2010 p438
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