Multimedia Tools and Applications

, Volume 76, Issue 8, pp 11003–11019 | Cite as

Local-to-global background modeling for moving object detection from non-static cameras

  • Aihua Zheng
  • Lei Zhang
  • Wei Zhang
  • Chenglong Li
  • Jin Tang
  • Bin Luo
Article

Abstract

This paper investigates efficient and robust moving object detection from non-static cameras. To tackle the motion of background caused by moving cameras and to alleviate the interference of noises, we propose a local-to-global background model for moving object detection. Firstly, motion compensation based local location-specific background model is deployed to roughly detect the foreground regions in non-static cameras. More specifically, the local background model is built for each pixel and represented by a set of pixel values drawn from its location and neighborhoods. Each pixel can be classified as foreground or background pixel according to the compensated background model based on the fast optical flow. Secondly, we estimate the global background model by the rough superpixel-based background regions to further separate foregrounds from background accurately. In particular, we use the superpixel to generate the initial background regions based on the detection results generated by local background model to alleviate the noises. Then, a Gaussian Mixture Model (GMM) is estimated for the backgrounds on superpixel level to refine the foreground regions. Extensive experiments on newly created dataset, including 10 challenging video sequences recorded in PTZ cameras and hand-held cameras, suggest that our method outperforms other state-of-the-art methods in accuracy.

Keywords

Background modeling Object detection Non-static cameras Motion compensation Random algorithm Superpixel processing Gaussian mixture model 

Notes

Acknowledgments

Our thanks to the support from the National Nature Science Foundation of China (61502006, 61472002), the Natural Science Foundation of Anhui Province (1508085QF127) and the Natural Science Foundation of Anhui Higher Education Institutions of China (KJ2014A015).

References

  1. 1.
    Achanta R, Shaji A, Smith K et al (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34(11):2274–2282CrossRefGoogle Scholar
  2. 2.
    Bao L, Song Y, Yang Q, et al (2012) An edge-preserving filtering framework for visibility restoration. 21st IEEE International Conference on Pattern Recognition (ICPR), pp 384–387Google Scholar
  3. 3.
    Bao LC, Yang QX, Jin HL (2014) Fast edge-preserving PatchMatch for large displacement optical flow. IEEE Trans Image Process 23(12):4996–5006MathSciNetCrossRefGoogle Scholar
  4. 4.
    Barnes C, Shechtman E, Finkelstein A et al (2009) PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans Graph 28(3):341–352CrossRefGoogle Scholar
  5. 5.
    Brox T, Malik J (2010) Object segmentation by long term analysis of point trajectories. In: Proc. European Conference on Computer Vision, vol 6315. pp 282–295Google Scholar
  6. 6.
    Dempster AP, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39(1):1–38MathSciNetMATHGoogle Scholar
  7. 7.
    Hosni A, Rhemann C, Bleyer M et al (2013) Fast cost-volume filtering for visual correspondence and beyond. IEEE Trans Pattern Anal Mach Intell 35(2):504–511CrossRefGoogle Scholar
  8. 8.
    Kim J, Wang X, Wang H et al (2013) Fast moving object detection with non-stationary background. MultimedTools Appl 67(1):311–335CrossRefGoogle Scholar
  9. 9.
    Li C, Hu S, Gao S, Tang J (2016) Real-time grayscale-thermal tracking via Laplacian sparse representation. In: Proceedings of International Conference on Multimedia ModelingGoogle Scholar
  10. 10.
    Li C, Lin L, Zuo W, et al (2015) SOLD: sub-optimal low-rank decomposition for efficient video segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 5519–5527Google Scholar
  11. 11.
    Liang Z, Wang M, Zhou X et al (2014) Salient object detection based on regions. Multimed Tools Appl 68(3):517–544CrossRefGoogle Scholar
  12. 12.
    Lin LL, Chen NR (2011) Moving objects detection based on gaussian mixture model and saliency map. Appl Mech Mater 2011(63–64):350–354CrossRefGoogle Scholar
  13. 13.
    Miao Q, Cao Y, Xia G, et al (2015) RBoost: label noise-robust boosting algorithm based on a nonconvex loss function and the numerically stable base learners. IEEE Trans Neural Netw Learn Syst 2015:1Google Scholar
  14. 14.
    Miao Q, Shi C, Xu P et al (2011) A novel algorithm of image fusion using shearlets. Opt Commun 284(6):1540–1547CrossRefGoogle Scholar
  15. 15.
    Narayana M, Hanson A et al (2013) Coherent motion segmentation in moving camera videos using optical flow orientations. IEEE International Conference on Computer Vision (ICCV), pp 1577–1584Google Scholar
  16. 16.
    Olivier B, Marc V (2011) Vibe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724MathSciNetCrossRefGoogle Scholar
  17. 17.
    Papazoglou A, Ferrari V (2013) Fast object segmentation in unconstrained video. Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 1777–1784Google Scholar
  18. 18.
    Patel MP, Parmar SK (2014) Moving object detection with moving background using optic flow. IEEE conference on Recent Advances and Innovations in Engineering (ICRAIE), pp 1–6Google Scholar
  19. 19.
    Schoenemann T, Cremers D (2008) High resolution motion layer decomposition using dual-space graph cuts. Proc IEEE Conf Comput Vis Pattern Recognit 2008:1–7Google Scholar
  20. 20.
    Shakeri M, Zhang H (2015) COROLA: a sequential solution to moving object detection using low-rank Approximation.arXiv preprint arXiv:1505.03566Google Scholar
  21. 21.
    Stauffer C, Grimson WEL (1999) Adaptive background mixture models for realtime tracking. In: CVPR. Proceedings of CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2–2246Google Scholar
  22. 22.
    Tao M, Bai J, Kohli P, et al (2012), SimpleFlow: a non-iterative, sublinear optical flow algorithm. Computer graphics forum. Blackwell Publishing Ltd 31(21):345–353Google Scholar
  23. 23.
    Toennies K, Rak M, Engel K (2014) Deformable part models for object detection in medical images. Biomed Eng Online 13(supp1):911–916Google Scholar
  24. 24.
    Unzueta L, Nieto M, Barandiaran J et al (2012) Adaptive multi-cue background subtraction for robust vehicle counting and classification. IEEE Trans Intell Transp Syst 13(2):527–540CrossRefGoogle Scholar
  25. 25.
    Vanogenbroeck M, Paquot O (2012) Background subtraction: experiments and improvements for ViBe. Computer Vision and Pattern Recognition Workshops (CVPRW), 2012 I.E. Computer Society Conference, pp 32–37Google Scholar
  26. 26.
    Vieux R, Jenny BP, Domenger JP et al (2012) Segmentation-based multi-class semantic object detection. Multimed Tools Appl 60(2):305–326CrossRefGoogle Scholar
  27. 27.
    Xu K, Zeng XL, Yan G (2012) Research on moving object detection based on improved gaussian mixture background model. Sci Mosaic 2012:12–15Google Scholar
  28. 28.
    Yang Q, Yang R, Davis J, et al (2007) Spatial-depth super resolution for range images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 1–8Google Scholar
  29. 29.
    Yoon KJ, Kweon IS (2006) Adaptive support-weight approach for correspondence search. IEEE Trans Pattern Anal Mach Intell 28(4):650–656CrossRefGoogle Scholar
  30. 30.
    Yu Y, Wang Q, Wang X, et al (2012) Trajectory stream mining framework facing to real time query processing. Chin J Sci Instrum 33(12)Google Scholar
  31. 31.
    Zeppelzauer M, Zaharieva M, Mitrovic D et al (2010) A novel trajectory clustering approach for motion segmentation. Lect Notes Comput Sci 2010(5916):433–443CrossRefGoogle Scholar
  32. 32.
    Zhang W, Li CL, Zheng AH, et al (2015). Motion compensation based fast moving object detection in dynamic background. Computer vision, Vol. 547. Springer Berlin Heidelberg, pp 247–256Google Scholar
  33. 33.
    Zhang G, Yuan Z, Liu Y et al (2015) Video object segmentation by integrating trajectories from points and regions. Multimed Tools Appl 74(21):9665–9696CrossRefGoogle Scholar
  34. 34.
    Zhang L, Zhou WD, Li FZ (2013) Kernel sparse representation-based classifier ensemble for face recognition. Multim Tools Appl 74(1):123–137CrossRefGoogle Scholar
  35. 35.
    Zhou X, Yang C, Yu W (2012) Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(3):597–610CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Aihua Zheng
    • 1
  • Lei Zhang
    • 1
  • Wei Zhang
    • 1
  • Chenglong Li
    • 1
  • Jin Tang
    • 1
    • 2
  • Bin Luo
    • 1
    • 2
  1. 1.School of Computer Science and TechnologyAnHui UniversityHefeiChina
  2. 2.Key Laboratory of Industry Image Processing and Analysis in Anhui ProvinceHefeiChina

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