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Video stabilization performance enhancement for low-texture videos

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Abstract

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.

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References

  1. Liu, F., Gleicher, M., Wang, J., Jin, H., Agarwala, A.: Subspace video stabilization. ACM Trans. Graph. 30(1), 4 (2011)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  3. Grundmann, M., Kwatra, V., Essa, I.: Auto-directed video stabilization with robust l1 optimal camera paths. In: Proc.CVPR, pp. 25–232 (2011)

  4. Zhang, Huang, : A global approach to fast video stabilization. IEEE Trans. Circ. Syst. 27(2), 225–235 (2017)

    Article  Google Scholar 

  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. 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)

  7. Bergen, J. R., Anandan, P., Hanna, K. J., Hingorani, R.: Hierarchical model-based motion estimation. In: Proceedings of ECCV, pp. 237–252 (1992)

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  12. Thomas, G.: Television motion measurement for data and other applications. NASA STI/Recon Technical Report N, vol. 88 (1987)

  13. Xu, L., Lin, X.: Digital image stabilization based on circular block matching. IEEE Trans. Consum. Electron. 52(2), 566–574 (2006)

    Article  Google Scholar 

  14. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  15. Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: Proceedings of ECCV, pp. 404–417 (2006)

  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)

    Article  MathSciNet  Google Scholar 

  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)

  18. Dong, J., Liu, H.: Video stabilization for strict real-time applications. IEEE Trans. Circ. Syst. Video Technol. 27(4), 716–724 (2017)

    Article  MathSciNet  Google Scholar 

  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. 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. 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)

  22. Erturk, S.: Digital image stabilization with sub-image phase correlation based global motion estimation. IEEE Trans. Consum. Electron. 49(4), 1320–1325 (2003)

    Article  Google Scholar 

  23. Ertürk, S.: Real-time digital image stabilization using Kalman filters. Real Time Imaging 8(4), 317–328 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  26. Barjatya, A.: Block matching algorithms for motion estimation. IEEE Trans. Evol. Comput. 8(3), 225–239 (2004)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  28. Rao, K.R., Kim, D.N., Hwang, J.J.: Fast Fourier Transform Algorithms and Applications. Springer, Berin (2011)

    MATH  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  31. Tekalp, A.M.: Digital Video Processing. Prentice Hall Press, Prentice (2015)

    Google Scholar 

  32. Gonzalez, R.: Improving phase correlation for image registration. In: Proceedings of ICIVC, pp. 488–493 (2011)

  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)

    Article  Google Scholar 

  34. Gonzalez, R. C., Woods, R. E.: Image processing. Digital image processing, vol. 2 (2007)

  35. Finch, T.: Incremental calculation of weighted mean and variance. Univ. Camb. 4, 11–5 (2009)

    Google Scholar 

  36. Morimoto, C., Chellappa, R.: Evaluation of image stabilization algorithms. In: Proceedings of ASSP, vol. 5, pp. 2789–2792 (1998)

  37. Xilinx, Axi multiport memorycontroller using the vivado tools. no. xapp789 (2012)

  38. Xilinx, 7 Series FPGA Datasheet. no. DS180(v2.2) (2016)

  39. Xilinx, Axi Video Direct Memory Access, LogiCore IP Product Guide, no. pg020 (2016)

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Acknowledgements

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

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  1. Department of Electronics and Communication, National Institute of Technology, Calicut, Kerala, 673601, India

    Supriya Unnikrishnan & G. Sreelekha

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  1. Supriya Unnikrishnan
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  2. G. Sreelekha
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Correspondence to Supriya Unnikrishnan.

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Unnikrishnan, S., Sreelekha, G. Video stabilization performance enhancement for low-texture videos. J Real-Time Image Proc 17, 1135–1152 (2020). https://doi.org/10.1007/s11554-019-00862-1

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