Video stabilization performance enhancement for low-texture videos

  • Supriya UnnikrishnanEmail author
  • G. Sreelekha
Original Research Paper


Digital video stabilization (DVS) aims to remove irregular global motion effects from an image sequence. This work aims at developing a real-time video stabilization algorithm for rectifying high-frequency jitter in marine surveillance applications. A DVS system consists of a global motion estimation system and motion correction system. The development of global motion estimation system resistant to failures in low texture videos is the primary goal. Due to the computational advantage and inherent properties, the phase correlation method is adopted as the basic global motion estimation algorithm. The basic algorithm is then modified to adapt to the varying texture content of the video sequences under consideration. An adaptive phase correlation-based global motion estimation is suggested and verified on the videos of varying textures.


Video stabilization Motion estimation Phase correlation 



We would like to acknowledge Central Research Laboratory,Bangalore for providing us the field data to enable us work on this research area.


  1. 1.
    Liu, F., Gleicher, M., Wang, J., Jin, H., Agarwala, A.: Subspace video stabilization. ACM Trans. Graph. 30(1), 4 (2011)CrossRefGoogle Scholar
  2. 2.
    Wang, Y.-S., Liu, F., Hsu, P.-S., Lee, T.-Y.: Spatially and temporally optimized video stabilization. IEEE Trans. Vis. Comput. Graph. 19(8), 1354–1361 (2013)CrossRefGoogle Scholar
  3. 3.
    Grundmann, M., Kwatra, V., Essa, I.: Auto-directed video stabilization with robust l1 optimal camera paths. In: Proc.CVPR, pp. 25–232 (2011)Google Scholar
  4. 4.
    Zhang, Huang, : A global approach to fast video stabilization. IEEE Trans. Circ. Syst. 27(2), 225–235 (2017)CrossRefGoogle Scholar
  5. 5.
    Liu, F., Gleicher, M., Jin, H., Agarwala, A.: Content-preserving warps for 3D video stabilization. ACM Trans. Graph. 28(3), 44 (2009)Google Scholar
  6. 6.
    Liu, S., Wang, Y., Yuan, L., Bu, J., Tan, P., Sun, J.: Video stabilization with a depth camera. In: Proceedings of CVPR, pp. 89–95 (2012)Google Scholar
  7. 7.
    Bergen, J. R., Anandan, P., Hanna, K. J., Hingorani, R.: Hierarchical model-based motion estimation. In: Proceedings of ECCV, pp. 237–252 (1992)Google Scholar
  8. 8.
    Su, Y., Sun, M.-T., Hsu, V.: Global motion estimation from coarsely sampled motion vector field and the applications. IEEE Trans. Circ. Syst. Video Technol. 15(2), 232–242 (2005)CrossRefGoogle Scholar
  9. 9.
    Chien, S.-L., Chen, C.-Y., Chao, W.-M., Huang, Y.-W., Chen, L.-G.: Analysis and hardware architecture for global motion estimation in mpeg-4 advanced simple profile. In: Proceedings of ISCAS, vol. 2, pp. II–II (2003)Google Scholar
  10. 10.
    Li, J., Xu, T., Zhang, K.: Real-time feature-based video stabilization on FPGA. IEEE Trans. Circ. Syst. Video Technol. 27(4), 907–919 (2017)CrossRefGoogle Scholar
  11. 11.
    Kumar, S., Azartash, H., Biswas, M., Nguyen, T.: Real-time affine global motion estimation using phase correlation and its application for digital image stabilization. IEEE Trans. Image Process. 20(12), 3406–3418 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Thomas, G.: Television motion measurement for data and other applications. NASA STI/Recon Technical Report N, vol. 88 (1987)Google Scholar
  13. 13.
    Xu, L., Lin, X.: Digital image stabilization based on circular block matching. IEEE Trans. Consum. Electron. 52(2), 566–574 (2006)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Proceedings of ECCV, pp. 404–417 (2006)Google Scholar
  16. 16.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Smolic, A., Hoeynck, M., Ohm, J.-R.: Low-complexity global motion estimation from p-frame motion vectors for mpeg-7 applications. In: Proceedings of ICIP, vol. 2, pp. 271–274 (2000)Google Scholar
  18. 18.
    Dong, J., Liu, H.: Video stabilization for strict real-time applications. IEEE Trans. Circ. Syst. Video Technol. 27(4), 716–724 (2017)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Shene, T.N., Sridharan, K., Sudha, N.: Real-time surf-based video stabilization system for an fpga-driven mobile robot. IEEE Trans. Ind. Electron. 63(8), 5012–5021 (2016)Google Scholar
  20. 20.
    Lim, A., Ramesh, B., Yang, Y., Xiang, C., Gao, Z., Lin, F.: Real-time optical flow-based video stabilization for unmanned aerial vehicles. J Real Time Image Process 2017, 1–11 (2017)Google Scholar
  21. 21.
    Sergieh, H.M., Zsigmond, E.E., Doller, M., Coquil, D., Pinon, J.M., Kosch, H.: Improving surf image matching using supervised learning. In: Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on, pp. 230–237. IEEE (2012)Google Scholar
  22. 22.
    Erturk, S.: Digital image stabilization with sub-image phase correlation based global motion estimation. IEEE Trans. Consum. Electron. 49(4), 1320–1325 (2003)CrossRefGoogle Scholar
  23. 23.
    Ertürk, S.: Real-time digital image stabilization using Kalman filters. Real Time Imaging 8(4), 317–328 (2002)CrossRefzbMATHGoogle Scholar
  24. 24.
    Yang, J., Schonfeld, D., Mohamed, M.: Robust video stabilization based on particle filter tracking of projected camera motion. IEEE Trans. Circ. Syst. Video Technol. 19(7), 945–954 (2009)CrossRefGoogle Scholar
  25. 25.
    Tarel, J.P., Hautiere, N., Cord, A., Gruyer, D., Halmaoui, H.: Improved visibility of road scene images under heterogeneous fog. In: Intelligent Vehicles Symposium (IV), 2010 IEEE, pp. 478–485. IEEE (2010)Google Scholar
  26. 26.
    Barjatya, A.: Block matching algorithms for motion estimation. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)CrossRefGoogle Scholar
  27. 27.
    Campo, F.B., Ruiz, F.L., Sappa, A.D.: Multimodal stereo vision system: 3d data extraction and algorithm evaluation. IEEE J. Sel. Top. Signal Process. 6(5), 437–446 (2012)CrossRefGoogle Scholar
  28. 28.
    Rao, K.R., Kim, D.N., Hwang, J.J.: Fast Fourier Transform Algorithms and Applications. Springer, Berin (2011)zbMATHGoogle Scholar
  29. 29.
    Hassen, W., Amiri, H.: Block matching algorithms for motion estimation. In: e-Learning in Industrial Electronics (ICELIE), 2013 7th IEEE International Conference on, pp. 136–139. IEEE (2013)Google Scholar
  30. 30.
    Reddy, B.S., Chatterji, B.N.: An fft-based technique for translation, rotation, and scale-invariant image registration. IEEE Trans. Image Process. 5(8), 1266–1271 (1996)CrossRefGoogle Scholar
  31. 31.
    Tekalp, A.M.: Digital Video Processing. Prentice Hall Press, Prentice (2015)Google Scholar
  32. 32.
    Gonzalez, R.: Improving phase correlation for image registration. In: Proceedings of ICIVC, pp. 488–493 (2011)Google Scholar
  33. 33.
    Nou-Shene, T., Pudi, V., Sridharan, K., Thomas, V., Arthi, J.: Very large-scale integration architecture for video stabilisation and implementation on a field programmable gate array-based autonomous vehicle. IET Comput. Vis. 9(4), 559–569 (2015)CrossRefGoogle Scholar
  34. 34.
    Gonzalez, R. C., Woods, R. E.: Image processing. Digital image processing, vol. 2 (2007)Google Scholar
  35. 35.
    Finch, T.: Incremental calculation of weighted mean and variance. Univ. Camb. 4, 11–5 (2009)Google Scholar
  36. 36.
    Morimoto, C., Chellappa, R.: Evaluation of image stabilization algorithms. In: Proceedings of ASSP, vol. 5, pp. 2789–2792 (1998)Google Scholar
  37. 37.
    Xilinx, Axi multiport memorycontroller using the vivado tools. no. xapp789 (2012)Google Scholar
  38. 38.
    Xilinx, 7 Series FPGA Datasheet. no. DS180(v2.2) (2016)Google Scholar
  39. 39.
    Xilinx, Axi Video Direct Memory Access, LogiCore IP Product Guide, no. pg020 (2016)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and CommunicationNational Institute of TechnologyCalicutIndia

Personalised recommendations