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Motion estimation using maximum sub-image and sub-pixel phase correlation on a DSP platform

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Abstract

Motion estimation is a key step for many video process systems, but it usually suffers from low precision in complex imaging scenarios, as well as the problem of real-time processing. This paper proposes an accurate and fast motion estimation algorithm using maximum sub-image and sub-pixel phase correlation implemented on a DSP platform. Firstly, a maximum sub-image is extracted to contain the image content as much as possible, which both satisfies the requirement of Fourier precondition and the wide coverage of complete background. And then, the extracted sub-image is down-sampled with median filter to increase the signal-noise-ratio and decrease the computation load. Secondly, a sub-pixel motion estimation is used to compensate the losing precision due to down-sampling, and keep the range of motion estimation. Finally, the proposed motion estimation algorithm is implemented on a single core TMS320C6678 DSP platform, and it is accelerated by applying multistage data cache and advanced data access. Experiments demonstrate the accuracy of the proposed motion estimation algorithm in complex scenarios. Meanwhile, it can achieve 7.4 ms/frame for sub-image with 512 × 512 pixels size and 29.6 ms/frame for sub-image with 1024 × 1024 pixels size, respectively.

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References

  1. Alba A, Arce-Santana E, Aguilar-Ponce RM, Campos-Delgado DU (2014) Phase-correlation guided area matching for realtime vision and video encoding. J Real-Time Image Proc 9(4):621–633

    Article  Google Scholar 

  2. Alba A, Vigueras-Gomez JF, Arce-Santana ER, Aguilar-Ponce RM (2015) Phase correlation with sub-pixel accuracy: a comparative study in 1D and 2D. Comput Vis Image Underst 137:76–87

    Article  Google Scholar 

  3. Balci M, Foroosh H (July 2006) Subpixel estimation of shifts directly in the Fourier domain. IEEE Trans Image Process 15(7):1965–1972

    Article  Google Scholar 

  4. Battiato S, Bruna AR, Puglisi G (2010) A robust block-based image/video registration approach for mobile imaging devices. IEEE Trans Multimedia 12(7):622–635

    Article  Google Scholar 

  5. Chen J, Xing M, Sun G-C, Li Z (2017) A 2-D space-variant motion estimation and compensation method for ultrahigh-resolution airborne stepped-frequency SAR with long integration time. IEEE Trans Geosci Remote Sens 55(11):6390–6401

    Article  Google Scholar 

  6. Dong Y, Long T, Jiao W, He G, Zhang Z (2018) A novel image registration method based on PhaseCorrelation using low-rank matrix factorization with mixture of Gaussian. IEEE Trans Geosci Remote Sens 56(1):446–460

    Article  Google Scholar 

  7. Douini Y, Riffi J, Mahraz MA, Tairi H (2017) Solving sub-pixel image registration problems using phase correlation and Lucas-Kanade optical flow method[C]. Intelligent Systems and Computer Vision. p 1–5

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

    Article  Google Scholar 

  9. Fang L, Au OC, Tang K et al (2012) Novel 2-D MMSE subpixel-based image down-sampling[J]. IEEE Trans Circuits Syst Video Technol 22(5):740–753

    Article  Google Scholar 

  10. Firouzi S, Joslin C (2013) Motion estimation in blurred frames using phase correlation. International Conference on Signal-Image Technology & Internet-Based Systems. p 201–206

  11. Igual FD, Botella G, García C et al (2013) Robust motion estimation on a low-power multi-core DSP. EURASIP Journal on Advances in Signal Processing 1:99

    Article  Google Scholar 

  12. Ismail Y, McNeely J, Shaaban M, Al Najjar M, Bayoumi MA (2010) A fast discrete transform architecture for frequency domain motion estimation. IEEE International Conference on Image Processing, p 1249–1252

  13. Keller Y, Shkolnisky Y, Averbuch A (2005) The angular difference function and its application to image registration. IEEE Transactions on Pattern Analysis & Machine Intelligence 27(6):969–976

    Article  Google Scholar 

  14. Kim SW, Yin S, Yun K, Choi JY (2014) Spatio-temporal weighting in local patches for direct estimation of camera motion in video stabilization. Comput Vis Image Underst 118:71–83

    Article  Google Scholar 

  15. Kumar S, Azartash H, Biswas M, Nguyen T (2011) Real-time affine global motion estimation using phase correlation and its application for digital image stabilization. IEEE Trans Image Process 20(12):3406–3418

    Article  MathSciNet  MATH  Google Scholar 

  16. Li J, Liu Y, Shuangli D, Wu P, Zhenyu X (2016) Hierarchical and adaptive phase correlation for precise disparity estimation of UAV images. IEEE Trans Geosci Remote Sens 54(12):7092–7104

    Article  Google Scholar 

  17. Lin W, Panusopone K, Baylon DM, Sun MT (2015) A fast sub-pixel motion estimation algorithm for H.264/AVC video coding. IEEE Trans Circuits Syst Video Technol 21(2):237–242

    Article  Google Scholar 

  18. Matsuo K, Hamada T, Miyoshi M, et al (2009) Accelerating phase correlation functions using GPU and FPGA. NASA/ESA Conference on Adaptive Hardware and Systems, San Francisco, p 433–438

  19. Monteiro E, Vizzotto B, Diniz C, Zatt B, Bampi S (2011) Applying CUDA architecture to accelerate full search block matching algorithm for high performance motion estimation in video encoding. IEEE International Symposium on Computer Architecture and High Performance Computing, Vitoria, p 128–135

  20. Reddy BS, Chatterji BN (1996) An FFT-based technique for translation, rotation and scale-invariant image registration. IEEE Trans Image Process 5(8):1266–1271

  21. Ren JC, Vlachosb T, Zhang Y, Zheng JB, Jiang JM (2014) Gradient-based subspace phase correlation for fast and effective image alignment. J Vis Commun Image Represent 25(7):1558–1565

    Article  Google Scholar 

  22. Rowekamp T, Platzner M, Peters L (1997) Specialized architectures for optical flow computation: a performance comparison of asic, dsp, and multi-dsp. In: Proceedings of the 8th ICSPAT, p 829–833

  23. Texas Instruments (2010) TMS320C66x DSP CPU and instruction and set reference guide. Texas, USA, Literature Number: SPRUGH7: 1–1–9–6

  24. Vasileios M, Ansgar S, Ramesh J, Mohan SK (2013) Real-life events in multimedia: detection, representation, retrieval, and applications. Multimed Tools Appl 70(1):1–6

    Google Scholar 

  25. Walha A, Wali A, Alimi AM AM (2015) Video stabilization with moving object detecting and tracking for aerial video surveillance. Multimed Tools Appl 74(17):6745–6767

    Article  Google Scholar 

  26. Wan X, Liu JG, Yan H (2015) The illumination robustness of phase correlation for image alignment. IEEE Trans Geosci Remote Sens 53(10):5746–5759

    Article  Google Scholar 

  27. Wang Z, Chen Y, Zhu Z, Zhao W (2016) An automatic panoramic image mosaic method based on graph model. Multimed Tools Appl 75(5):2725–2740

    Article  Google Scholar 

  28. Wang H, Zhao J, Zhao J, Dong F, Pan Z, Feng Y (2017) Position detection method of linear motor mover based on extended phase correlation algorithm. IET Sci Meas Technol 11(7):921–928

    Article  Google Scholar 

  29. Wu XQ, Zhao QS, Bu W (2014) A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors. Pattern Recogn 47(10):3314–3326

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Key Research and Development Plan (No.2016YFC0801002), the NSFC (No.61876014, No.61632001, No.61772054) and Army Equipment Research Project (No.301020203).

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, 100191, China

    Jin Zheng

  2. Beijing Institute of Astronautical Systems Engineering, Beijing, 100076, China

    Bo Zhai

  3. Beijing Key Laboratory of Digital Media, Beihang University, Beijing, 100191, China

    Yue Wang, Peisong Guo & Yongfei Zhang

Authors
  1. Jin Zheng
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  2. Bo Zhai
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  3. Yue Wang
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  4. Peisong Guo
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  5. Yongfei Zhang
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Correspondence to Bo Zhai.

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Zheng, J., Zhai, B., Wang, Y. et al. Motion estimation using maximum sub-image and sub-pixel phase correlation on a DSP platform. Multimed Tools Appl 78, 19019–19043 (2019). https://doi.org/10.1007/s11042-018-7146-3

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  • DOI: https://doi.org/10.1007/s11042-018-7146-3

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