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.
Similar content being viewed by others
References
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):2274–2282
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 384–387
Bao LC, Yang QX, Jin HL (2014) Fast edge-preserving PatchMatch for large displacement optical flow. IEEE Trans Image Process 23(12):4996–5006
Barnes C, Shechtman E, Finkelstein A et al (2009) PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans Graph 28(3):341–352
Brox T, Malik J (2010) Object segmentation by long term analysis of point trajectories. In: Proc. European Conference on Computer Vision, vol 6315. pp 282–295
Dempster AP, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc 39(1):1–38
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):504–511
Kim J, Wang X, Wang H et al (2013) Fast moving object detection with non-stationary background. MultimedTools Appl 67(1):311–335
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 Modeling
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 5519–5527
Liang Z, Wang M, Zhou X et al (2014) Salient object detection based on regions. Multimed Tools Appl 68(3):517–544
Lin LL, Chen NR (2011) Moving objects detection based on gaussian mixture model and saliency map. Appl Mech Mater 2011(63–64):350–354
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 2015:1
Miao Q, Shi C, Xu P et al (2011) A novel algorithm of image fusion using shearlets. Opt Commun 284(6):1540–1547
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 1577–1584
Olivier B, Marc V (2011) Vibe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724
Papazoglou A, Ferrari V (2013) Fast object segmentation in unconstrained video. Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp 1777–1784
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 1–6
Schoenemann T, Cremers D (2008) High resolution motion layer decomposition using dual-space graph cuts. Proc IEEE Conf Comput Vis Pattern Recognit 2008:1–7
Shakeri M, Zhang H (2015) COROLA: a sequential solution to moving object detection using low-rank Approximation.arXiv preprint arXiv:1505.03566
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 2–2246
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):345–353
Toennies K, Rak M, Engel K (2014) Deformable part models for object detection in medical images. Biomed Eng Online 13(supp1):911–916
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):527–540
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 32–37
Vieux R, Jenny BP, Domenger JP et al (2012) Segmentation-based multi-class semantic object detection. Multimed Tools Appl 60(2):305–326
Xu K, Zeng XL, Yan G (2012) Research on moving object detection based on improved gaussian mixture background model. Sci Mosaic 2012:12–15
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 1–8
Yoon KJ, Kweon IS (2006) Adaptive support-weight approach for correspondence search. IEEE Trans Pattern Anal Mach Intell 28(4):650–656
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)
Zeppelzauer M, Zaharieva M, Mitrovic D et al (2010) A novel trajectory clustering approach for motion segmentation. Lect Notes Comput Sci 2010(5916):433–443
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 247–256
Zhang G, Yuan Z, Liu Y et al (2015) Video object segmentation by integrating trajectories from points and regions. Multimed Tools Appl 74(21):9665–9696
Zhang L, Zhou WD, Li FZ (2013) Kernel sparse representation-based classifier ensemble for face recognition. Multim Tools Appl 74(1):123–137
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):597–610
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).
Author information
Authors and Affiliations
Corresponding author
Additional information
Part of work in the manuscript has been accepted and published on CCCV 2015.
Rights and permissions
About this article
Cite this article
Zheng, A., Zhang, L., Zhang, W. et al. Local-to-global background modeling for moving object detection from non-static cameras. Multimed Tools Appl 76, 11003–11019 (2017). https://doi.org/10.1007/s11042-016-3565-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-016-3565-1