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An adaptive graph cut algorithm for video moving objects detection

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Abstract

The algorithms based on graph cut have the advantage to detect the moving objects effectively and robustly. The main trouble of the algorithm based on graph cut is that its model parameters will be determined empirically. In this paper, a novel algorithm of adaptive graph cut is proposed to detect video moving objects. Based on Markov random field model, the proposed algorithm uses the numbers of moving objects pixels and objectives-background pixel-pairs to describe the geometric features of the moving objects. And the relationship between the geometric features of the moving objects and the model parameters are set up. In this paper, the model parameters are adaptively optimized through the extraction and prediction of the geometric features of moving objects. Then the detection based on the graph cut is preformed on ROI, which well achieves the balance between the computation and accuracy. Finally, the experimental results show the proposed algorithm can hold the details of moving objects more effectively compared with other algorithms, and improve the detection performance of moving object in the video surveillance.

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References

  1. Amato A, Mozerov M, Xavier Roca X, Gonzàlez J (2010) Robust real-time background subtraction based on local neighborhood patterns. EURASIP J Adv Signal Process, pp. 1–9

  2. Bouttefroy PLM, Bouzerdoum A, Phung SL, Beghdadi A (2010) On the analysis of background subtraction techniques using Gaussian mixture models. In: Proceedings, IEEE International Conference on Acoustics Speech and Signal Processing, pp. 4042–4045

  3. Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans PAMI 26(9):1124–1137

    Article  Google Scholar 

  4. Delong A, Boykov Y (2008) A scalable graph-cut algorithm for ND grids. In: Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8

  5. Freedman D, Zhang T (2005) Interactive graph cut based segmentation with shape priors. In: Proceedings, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 755–761

  6. Fukuchi K, Miyazato K, Kimura A, Takagi S, Yamato J (2009) Saliency-based video segmentation with graph cuts and sequentially updated priors. In: Proceedings, IEEE International Conference on Multimedia and Expo, pp. 638–641

  7. Garrett Z, Saito H (2008) Live video object tracking and segmentation using graph cuts. In: Proceedings, IEEE International Conference on image processing, pp: 1576–1579

  8. Greig D, Porteous B, Seheult A (1989) Exact maximum a posteriori estimation for binary images. J R Stat Soc Ser B 51(2):271–279

    Google Scholar 

  9. Juan O, Boykov Y (2006) Active graph cuts. In: Proceedings, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1, pp. 1023–1029

  10. Kohli P, Torr PHS (2005) Efficiently solving dynamic Markov random fields using graph cuts. In: Proceedings, IEEE International Conference on Computer Vision, 2, pp. 922–929

  11. Li Y, Sun J, Shum HY (2005) Video object cut and paste. ACM Trans Graph 24(3):595–600

    Article  Google Scholar 

  12. Li Y, Sun J, Tang CK, Shum HY (2004) Lazy snapping. ACM Trans Graph 23(3):303–308

    Article  Google Scholar 

  13. Lombaert H, Sun Y, Grady L, Xu C (2005) A multilevel banded graph cuts method for fast image segmentation. In: Proceedings, IEEE International Conference on Computer Vision, pp. 259–265

  14. Lu W, Yung NHC (2010) Extraction of moving objects from their background based on multiple adaptive thresholds and boundary evaluation. IEEE Trans ITS 11(1):40–51

    Google Scholar 

  15. Nagahashi T, Fujiyoshi H, Kanade T (2007) Image segmentation using iterated graph cuts based on multi-scale smoothing. Lect Notes Comput Sci 4844:806–816

    Article  Google Scholar 

  16. Nagahashi T, Fujiyoshi H, Kanade (2010) Video segmentation using iterated graph cuts based on spatio-temporal volumes. Lect Notes Comput Sci 5995:655–666

    Article  Google Scholar 

  17. Pal A, Schaefer G, Celebi ME (2010) Robust codebook-based video background subtraction. In: Proceedings, IEEE International Conference on Acoustics Speech and Signal Processing, pp. 1146–1149

  18. Rother C, Kolmogorov V, Blake A (2004) “GrabCut” - Interactive foreground extraction using iterated graph cuts. In: Proceedings, ACM SIGGRAPH, pp. 309–314

  19. Vosters LPJ, Shan C-F, Gritti T (2010) Background subtraction under sudden illumination changes. In: Proceedings, IEEE Conference on AVSS, pp. 384–391

  20. Wang C-H, Guan L (2008) Graph cut video object segmentation using histogram of oriented gradients. In: Proceedings, IEEE International Symposium on Circuits and Systems, pp. 2590–2593

  21. Wang J-J, Xu W, Zhu S-H, Gong Y-H (2007) Efficient video object segmentation by graph-cut. In: Proceedings, IEEE International Conference on Multimedia and Expo, pp. 496–499

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Acknowledgments

This work is supported by Zhejiang Provincial Natural Science Foundation of China (No. LY12F01003, LQ13F010014), and partial supported by the National Natural Science Foundation of China (No. 61172134).

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Authors and Affiliations

  1. College of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China

    Chunsheng Guo & Dan Liu

  2. College of Automation Engineering, Hangzhou Dianzi University, Hangzhou, China

    YunFei Guo & Yao Sun

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  1. Chunsheng Guo
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  2. Dan Liu
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  3. YunFei Guo
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  4. Yao Sun
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Correspondence to Chunsheng Guo.

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Guo, C., Liu, D., Guo, Y. et al. An adaptive graph cut algorithm for video moving objects detection. Multimed Tools Appl 72, 2633–2652 (2014). https://doi.org/10.1007/s11042-013-1566-x

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