Skip to main content
SpringerLink
Log in

Robust optical flow estimation based on wavelet

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Concentrating on the issue that the systematic error caused by variation of illumination conditions and sensor noise leads to poor robustness and low accuracy of optical flow calculation, based on the wavelet multi-resolution theory, a robust optical flow estimation method is developed in this paper. With the multi-resolution characteristics of wavelet, the systematic error is incorporated into the calculation of optical flow to improve the robustness and estimation accuracy. In what follows, the total least squares method can be exploited to solve the obtained overdetermined wavelet optical flow equations such that the optical flow vector can be achieved. As compared to the traditional Lucas–Kanade approach, Horn–Schunck method, and the optical flow estimation algorithm in omnidirectional images using wavelet approach, simulation results show that the proposed algorithm can significantly improve the accuracy of optical flow estimation and the robustness of the optical flow field.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1–3), 185–203 (1981)

    Article  Google Scholar 

  2. Kajo, I., Malik, A.S., Kamel, N.: Motion estimation of crowd flow using optical flow techniques: a review. In: International Conference on Signal Processing and Communication Systems, pp. 1–9. IEEE (2016)

  3. Goppert, J., Yantek, S., Hwang, I.: Invariant Kalman filter application to optical flow based visual odometry for UAVs. In: Ninth International Conference on Ubiquitous and Future Networks, pp. 99–104. IEEE, Milan, Italy (2017)

  4. Peng, Y., Chen, Z., Wu, Q.M.J., et al.: Traffic flow detection and statistics via improved optical flow and connected region analysis. Signal Image Video Process. 12(1), 99–105 (2018)

    Article  Google Scholar 

  5. Khalid, M., Penard, L., Memin, E.: Application of optical flow for river velocimetry. In: IGARSS 2017—2017 IEEE International Geoscience and Remote Sensing Symposium, pp. 6243–6246. IEEE (2017)

  6. Lazcano, V., Molina, M.: Modification of the optical flow Horn–Schunck estimation incorporating exaustive search. In: Electrical, Electronics Engineering, Information and Communication Technologies, pp. 873–878. IEEE (2016)

  7. Douini, Y., Riffi, J., Mahraz, A.M., et al.: An image registration algorithm based on phase correlation and the classical Lucas–Kanade technique. Signal Image Video Process. 11(11), 1–8 (2017)

    Google Scholar 

  8. Drulea, M., Nedevschi, S.: Total variation regularization of local–global optical flow. In: International IEEE Conference on Intelligent Transportation Systems, pp. 318–323. IEEE, Washington, DC, USA (2011)

  9. Douini, Y., Riffi, J., Mahraz, M.A., et al.: Solving sub-pixel image registration problems using phase correlation and Lucas–Kanade optical flow method. In: Intelligent Systems and Computer Vision (2017)

  10. Magarey, J., Kingsbury, N.: Motion estimation using a complex-valued wavelet transform. IEEE Trans. Signal Process. 46(4), 1069–1084 (2002)

    Article  MathSciNet  Google Scholar 

  11. Dérian, P., Almar, R.: Wavelet-based optical flow estimation of instant surface currents from shore-based and UAV videos. IEEE Trans. Geosci. Remote Sens. 55(10), 5790–5797 (2017)

    Article  Google Scholar 

  12. Afdhal, R., Ejbali, R., Zaied, M., et al.: Emotion recognition using features distances classified by wavelets network and trained by fast wavelets transform. In: International Conference on Hybrid Intelligent Systems, pp. 238–241. IEEE (2015)

  13. Demonceaux, C., Kachi-Akkouche, D.: Optical flow estimation in omnidirectional images using wavelet approach. In: Computer Vision and Pattern Recognition Workshop, 2003. CVPRW ‘03. Conference on EEE, Madison, Wisconsin, USA, p. 76 (2003)

  14. Niaz, M.T., Imdad, F., Kim, S., et al.: Total least-square-based receiver for asymmetrically clipped optical-orthogonal frequency divisional multiplexing visible light communication system. IET Optoelectron. 11(4), 129–133 (2017)

    Article  Google Scholar 

  15. Wu, H., Chen, S., Zhang, Y., et al.: Robust structured total least squares algorithm for passive location. J. Syst. Eng. Electron. 26(5), 946–953 (2015)

    Article  Google Scholar 

  16. Kukde, R., Manikandan, M.S., Panda, G.: Reduced complexity diffusion filtered x least mean square algorithm for distributed active noise cancellation. Signal Image Video Process. 5, 1–9 (2019)

    Google Scholar 

  17. Abatzoglou, T.J., Lam, L.K.: Direction finding using uniform arrays and the constrained total least squares method. In: Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers, pp. 57–578. IEEE (1991)

  18. Barron, J.L., Fleet, D.J., Chemin, S.S.: Performance of optical flow techniques. In: Computer Vision and Pattern Recognition, 1992. Proceedings CVPR ‘92. 1992 IEEE Computer Society Conference on, pp. 236–242. IEEE, Champaign, IL, USA (2002)

  19. Cella, G.: Thermal noise correlations and subtraction. Phys. Lett. A 382(33), 2269–2274 (2018)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work is partially supported by the National Natural Science Foundation of China under Grant 61301258 and China Postdoctoral Science Foundation Funded Project under Grant No. 2016M590218.

Author information

Authors and Affiliations

  1. Liaoning Engineering Laboratory of BeiDou High-Precision Location Service, Dalian University, Dalian, 116622, China

    Jia Zheng, Hongyan Wang & Bingnan Pei

  2. Dalian Key Laboratory of Environmental Perception and Intelligent Control, Dalian University, Dalian, 116622, China

    Jia Zheng, Hongyan Wang & Bingnan Pei

Authors
  1. Jia Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongyan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bingnan Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongyan Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, J., Wang, H. & Pei, B. Robust optical flow estimation based on wavelet. SIViP 13, 1303–1310 (2019). https://doi.org/10.1007/s11760-019-01476-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01476-7

Keywords

Search

Navigation