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Robust global motion estimation for video security based on improved k-means clustering

  • Minghu Wu
  • Xiang Li
  • Cong LiuEmail author
  • Min Liu
  • Nan Zhao
  • Juan Wang
  • Xiangkui Wan
  • Zheheng Rao
  • Li Zhu
Original Research

Abstract

The global motion vectors estimation is the most critical step for eliminating undesirable disturbances in unsafe video. In this paper, we proposed a novel global motion estimation approach based on improved K-means clustering algorithm to acquire trustworthy sequences. Firstly, the speeded up robust feature algorithm is employed to match feature points between two adjacent frames, and then we calculate the motion vectors of these matching points. Secondly, to remove the local motion vectors and reduce redundancy from the motion vectors, an improved K-means clustering algorithm is proposed. Thirdly, by using matching points from richest cluster, global motion vectors are calculated by homography transformation. The experimental simulation results demonstrate that the proposed method can obtain significantly higher computational efficiency and superior video security performance than traditional approaches.

Keywords

K-means clustering Motion estimation Global motion vectors Video security 

Notes

Acknowledgements

This research was supported by National Natural Science Foundation of China (61471162, 61501178, 61601177, 61571182); Program of International science and technology cooperation (2015DFA10940); Science and technology support program (R & D) project of Hubei Province (2015BAA115); PhD Research Startup Foundation of Hubei University of Technology (BSQD14028); Open Foundation of Hubei Collaborative Innovation Center for High-Efficiency Utilization of Solar Energy (HBSKFZD2015005, HBSKFTD2016002); Science and Technology Research Program of Hubei Provincial Department of Education(Q20171401).

References

  1. Pradidtong-Ngam C, Natwichai J (2011) Content-based video search on peer-to-peer networks. Int J Grid Util Comput 2(3):234–242.  https://doi.org/10.1504/ijguc.2011.042045 CrossRefGoogle Scholar
  2. Shen Y, Xu Q, Xu Q, Zhang Z (2012) The multimedia service session handoff method in heterogeneous wireless networks. Int J Grid Util Comput 3(1):68–77.  https://doi.org/10.1504/ijguc.2012.045700 CrossRefGoogle Scholar
  3. Wang XA, Ma J, Yang X (2015) A new proxy re-encryption scheme for protecting critical information systems. J Ambient Intell Hum Comput 6(6):699–711.  https://doi.org/10.1007/s12652-015-0261-3 CrossRefGoogle Scholar
  4. Wang J, Li T, Shi YQ, Lian S, Ye J (2016) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimedia Tools Appl.  https://doi.org/10.1007/s11042-016-4153-0 Google Scholar
  5. Leu JS, Lin WH, Tzeng HJ, Chen CF, Lin MS (2012) Adaptive frame synchronization for surveillance system across a heterogeneous network. Eng Appl Artif Intell 25(7):1349–1354.  https://doi.org/10.1016/j.engappai.2012.02.001 CrossRefGoogle Scholar
  6. Lai WP, Fu WH (2012) Spm: split piecewise mapping for high quality wireless video delivery. Int J Sp Based Situat Comput 2(3):187–200.  https://doi.org/10.1504/IJSSC.2012.048899 CrossRefGoogle Scholar
  7. Wang J, Lian S, Shi YQ (2015) Hybrid multiplicative multi-watermarking in dwt domain. Multidimens Syst Signal Process 28(2):1–20.  https://doi.org/10.1007/s11045-015-0363-2 zbMATHGoogle Scholar
  8. Ma T, Wang Y, Tang M, Cao J, Tian Y, Al-Dhelaan A et al (2016) Led:a fast overlapping communities detection algorithm based on structural clustering. Neurocomputing 207:488–500.  https://doi.org/10.1016/j.neucom.2016.05.020 CrossRefGoogle Scholar
  9. Sato K, Ishizuka S, Nikami A, Sato M (1993) Control techniques for optical image stabilizing system. IEEE Trans Consum Electron 39(3):461–466.  https://doi.org/10.1109/30.234621 CrossRefGoogle Scholar
  10. Pinto B, Anurenjan PR (2011) Video stabilization using Speeded Up Robust Features. Int Conf Commun Signal Process IEEE.  https://doi.org/10.1109/ICCSP.2011.5739378 Google Scholar
  11. Cheng HY, Weng CC, Chen YY (2012) Vehicle detection in aerial surveillance using dynamic bayesian networks. IEEE Trans Image Process A Publ IEEE Signal Process Soc 21(4):2152.  https://doi.org/10.1109/TIP.2011.2172798 MathSciNetCrossRefzbMATHGoogle Scholar
  12. Amanatiadis AA, Andreadis I (2010) Digital image stabilization by independent component analysis. IEEE Trans Instrum Meas 59(7):1755–1763.  https://doi.org/10.1109/TIM.2009.2028216 CrossRefGoogle Scholar
  13. Xu L, Lin X (2006) Digital image stabilization based on circular block matching. IEEE Trans Consum Electron 52(2):566–574.  https://doi.org/10.1109/TCE.2006.1649681 CrossRefGoogle Scholar
  14. Tian S, Zhao P, Wang N, Wang C (2010) Aims at moving objects’ improvement based on gray projection of algorithm of the electronic image stabilization. In: International congress on image and signal processing, vol 5, pp 2483–2487. IEEE.  https://doi.org/10.1109/CISP.2010.5647924
  15. Matsushita Y, Ofek E, Ge W, Tang X, Shum HY (2006) Full-frame video stabilization with motion inpainting. IEEE Trans Pattern Anal Mach Intell 28(7):1150.  https://doi.org/10.1109/TPAMI.2006.141 CrossRefGoogle Scholar
  16. Yang J, Dan S, Mohamed M (2009) Robust video stabilization based on particle filter tracking of projected camera motion. IEEE Trans Circ Syst Video Technol 19(7):945–954.  https://doi.org/10.1109/TCSVT.2009.2020252 CrossRefGoogle Scholar
  17. Wang BR, Jin YL, Shao DL, Xu Y (2013) Design of jitter compensation algorithm for robot vision based on optical flow and kalman filter. Sci World J 2014(4):130806.  https://doi.org/10.1155/2014/130806 Google Scholar
  18. Xu J, Chang HW, Yang S, Wang M (2012) Fast feature-based video stabilization without accumulative global motion estimation. IEEE Trans Consum Electron 58(3):993–999.  https://doi.org/10.1109/TCE.2012.6311347 CrossRefGoogle Scholar
  19. Kim SK, Kang SJ, Wang TS, Ko SJ (2013) Feature point classification based global motion estimation for video stabilization. IEEE Trans Consum Electron 59(1):267–272.  https://doi.org/10.1109/TCE.2013.6490269 CrossRefGoogle Scholar
  20. Karimi NF, Lindenbergh R (2014) Sequential and automatic image-sequence registration of road areas monitored from a hovering helicopter. Sensors 14(9):16630–16650.  https://doi.org/10.3390/s140916630 CrossRefGoogle Scholar
  21. Xie X, Xu Y, Liu Q, Hu F, Cai T, Jiang N et al (2015) A study on fast sift image mosaic algorithm based on compressed sensing and wavelet transform. J Ambient Intell Hum Comput 6(6):835–843.  https://doi.org/10.1007/s12652-015-0319-2 CrossRefGoogle Scholar
  22. Yin X, Kim DH, Hong CP, Kim CG, Kim KJ, Kim SD (2015) Advanced feature point transformation of corner points for mobile object recognition. Multimedia Tools Appl 74(16):6541–6556.  https://doi.org/10.1007/s11042-014-2241-6 CrossRefGoogle Scholar
  23. Shene TN, Sridharan K, Sudha N (2016) Real-time surf-based video stabilization system for an fpga-driven mobile robot. IEEE Trans Ind Electron 63(8):5012–5021.  https://doi.org/10.1109/TIE.2016.2551684 Google Scholar
  24. Cheng X, Hao Q, Xie M (2016) A comprehensive motion estimation technique for the improvement of eis methods based on the surf algorithm and kalman filter. Sensors 16(4):486.  https://doi.org/10.3390/s16040486 CrossRefGoogle Scholar
  25. Li J, Li X, Yang B, Sun X (2017) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forens Secur 10(3):507–518.  https://doi.org/10.1109/TIFS.2014.2381872 Google Scholar
  26. Zhou Z, Wu QJ, Huang F, Sun X (2017) Fast and accurate near-duplicate image elimination for visual sensor networks. Int J Distrib Sensor Netw 13, 2(2017-2-01), 13(2):155014771769417Google Scholar
  27. Liu F, Gleicher M, Jin H, Agarwala A (2009) Content-preserving warps for 3D video stabilization. In: ACM SIGGRAPH, vol 28, p 44. ACM.  https://doi.org/10.1145/1531326.1531350
  28. Yoon B, Choi K, Ra M, Kim WY (2015) Real-time full-view 3d human reconstruction using multiple rgb-d cameras. Ieie Trans Smart Process Comput 4(4):224–230.  https://doi.org/10.5573/ieiespc.2015.4.4.224 CrossRefGoogle Scholar
  29. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110.  https://doi.org/10.1023/B:VISI.0000029664.99615.94 MathSciNetCrossRefGoogle Scholar
  30. Ahn H, Lee YH (2015) Performance analysis of object recognition and tracking for the use of surveillance system. J Ambient Intell Hum Comput.  https://doi.org/10.1007/12652-015-0325-4 Google Scholar
  31. Rublee E, Rabaud V, Konolige K, Bradski G (2011) ORB:An efficient alternative to SIFT or SURF. In: IEEE International conference on computer vision, ICCV 2011, Barcelona, Spain, November, vol 58, pp 2564–2571. DBLP.  https://doi.org/10.1109/ICCV.2011.6126544
  32. Ortiz R (2012) FREAK: Fast retina keypoint. In: Computer vision and pattern recognition, vol 157, pp 510–517. IEEE.  https://doi.org/10.1109/CVPR.2012.6247715

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Minghu Wu
    • 1
    • 2
  • Xiang Li
    • 1
    • 2
  • Cong Liu
    • 1
    • 2
    Email author
  • Min Liu
    • 1
    • 2
  • Nan Zhao
    • 1
    • 2
  • Juan Wang
    • 1
    • 2
  • Xiangkui Wan
    • 1
    • 2
  • Zheheng Rao
    • 1
    • 2
  • Li Zhu
    • 1
    • 2
  1. 1.Hubei Key Laboratory for High-efficiency Utilization of Solar Energy and Operation Control of Energy Storage SystemHubei University of TechnologyWuhanPeople’s Republic of China
  2. 2.Hubei Collaborative Innovation Center for High-efficiency Utilization of Solar EnergyHubei University of TechnologyWuhanPeople’s Republic of China

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