Skip to main content
SpringerLink
Log in

A least-squares method for simultaneous synchronization and relative calibration of overlapped videos

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

Abstract

In this paper, a straightforward mathematical model is proposed to synchronize and estimate the relative parameters of videos taken with a fixed relative orientation. The foundation of this model was the well-known coplanarity condition that prevails between matched points of two perspective images. Nevertheless, the synchronization problem has also been incorporated into it by making the matched points dependent on time. In this method, the required control data provides by tracking the positions of moving points in the temporal and spatial overlaps of the videos. Also, the unknown parameters are estimated through the least-squares estimation of a constrained system of linearized equations. The results of implementations on different datasets have demonstrated the efficiency of the proposed method in the temporal and relative calibration of stereo videos; as it has reached on average to the one frame accuracy in synchronization and 4.3 pixels precision in generalization of relative calibrations.

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

Similar content being viewed by others

References

  1. Ortiz-Coder, P., Cabecera, R.: Accurate 3d reconstruction using a videogrammetric device for heritage scenarios. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. XLVI-M-1–2021, 499–506 (2021)

    Article  Google Scholar 

  2. Nocerino, E., Menna, F., Remondino, F.: Comparison between single and multi-camera view videogrammetry for estimating 6DOF of a rigid body. Proc. Range Imaging Appl. (2015). https://doi.org/10.1117/12.2184977

    Article  Google Scholar 

  3. Elharrouss, A., Almaadeed, N., Al-Maadeed, S.: A review of video surveillance systems. J. Vis. Commun. Image Represent. 77(103116), 1–10 (2021). https://doi.org/10.1016/j.jvcir.2021.103116

    Article  Google Scholar 

  4. Zheng, X., Hu, C., Mei, L.: Video structured description technology based intelligence analysis of surveillance videos for public security applications. Multimed. Tools Appl. 75(19), 12155–12172 (2016)

    Article  Google Scholar 

  5. Brilakis, I., Fathi, H., Rashidi, A.: Progressive 3D reconstruction of infrastructure with videogrammetry. Autom. Constr. 20(7), 884–895 (2011). https://doi.org/10.1016/j.autcon.2011.03.005

    Article  Google Scholar 

  6. Zhen, Y., Xiaohua, T., Yusheng, X., Sa, G., Shijie, L., Huan, X., Peng, C., Wensheng, L., Xianglei, L.: An Improved subpixel phase correlation method with application in videogrammetric monitoring of shaking table tests. Photogramm. Eng. Remote. Sens. 84(9), 579–592 (2018). https://doi.org/10.14358/PERS.84.9.579

    Article  Google Scholar 

  7. Herráez, J., Martínez, J.C., Coll, E., Martín, M.T., Rodríguez, J.: 3D modeling by means of videogrammetry and laser scanners for reverse engineering. Measurement 87, 216–227 (2016). https://doi.org/10.1016/j.measurement.2016.03.005

    Article  Google Scholar 

  8. Lee, E.K., Ho, Y.S.: Generation of high-quality depth maps using hybrid camera system for 3-D video. J. Vis. Commun. Image Represent. 22(1), 73–84 (2011). https://doi.org/10.1016/j.jvcir.2010.10.006

    Article  Google Scholar 

  9. Sinha, S.N., Pollefeys, M.: Camera network calibration and synchronization from silhouettes in archived video. Int. J. Comput. Vis. 87, 266–283 (2010). https://doi.org/10.1007/s11263-009-0269-2

    Article  Google Scholar 

  10. Ma, Y., Soatto, S., Kosecka J., Shankar Sastry, S.: An Invitation to 3-D Vision (From Images to Geometric Models), Springer, New York, p. 528 (2004). https://doi.org/10.1007/978-0-387-21779-6

  11. Ganjali, A., Safdarinezhad, A.: A method for automatic temporal and relative calibration of the amateur cameras to produce 3D videos. J. Mach. Vis. Image Proc. (In Persian) 9, 77–91 (2022)

    Google Scholar 

  12. Shrestha, P., Barbieri, M., Weda, H., Sekulovski, D.: Synchronization of multiple camera videos using audio-visual features. IEEE Trans. Multimed. 12(1), 79–92 (2010). https://doi.org/10.1109/TMM.2009.2036285

    Article  Google Scholar 

  13. Brito, D.N., Pádua, F.L.C., Pereira, G.A.S., Carceroni, R.L.: Temporal synchronization of non-overlapping videos using known object motion. Pattern Recogn. Lett. 32(1), 38–46 (2011). https://doi.org/10.1016/j.patrec.2010.02.01.1

    Article  Google Scholar 

  14. Elhayek, A., Stoll, C., Hasler, N., Kim, K.I., Seidel, H., Theobalt, C.: Spatio-temporal motion tracking with unsynchronized cameras. IEEE Conf. Comput. Vis. Pattern Recogn. (2012). https://doi.org/10.1109/CVPR.2012.6247886

    Article  Google Scholar 

  15. Laganiere, W.R., Bose, P.: temporal synchronization of video sequences in theory and in practice. C2005 Seventh IEEE Workshops Appl Comput Vis (WACV/MOTION’05) 1, 132–137 (2005). https://doi.org/10.1109/ACVMOT.2005.114

    Article  Google Scholar 

  16. Meng, Z., Kong, X., Meng, L., Tomiyama, H.: Stereo vision-based depth estimation. In: Chiplunkar, N., Fukao T. (eds) Advances in Artificial Intelligence and Data Engineering. Advances in Intelligent Systems and Computing, Vol. 1133, Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-3514-7_90

  17. Chavent, G.: Nonlinear Least Squares for Inverse Problems: Theoretical Foundations and Step-by-Step Guide for Applications. Springer (2010)

  18. Nair, M.T., Singh, A.: Linear Algebra, Springer, Singapore, p. 341 (2018). https://doi.org/10.1007/978-981-13-0926-7

  19. Yuanyuan, S., Yongming, W., Lili, G., Zhongsong, M., Shan, J.: The comparison of optimizing SVM by GA and grid search. In: 13th IEEE International Conference on Electronic Measurement and Instruments (ICEMI), pp. 354–360 (2017). https://doi.org/10.1109/ICEMI.2017.8265815

  20. Shi, G., Xu, X., Dai, Y.: SIFT feature point matching based on improved RANSAC algorithm. In: 5th international conference on intelligent human-machine systems and cybernetics, pp. 474–477 (2013). https://doi.org/10.1109/IHMSC.2013.119

Download references

Author information

Authors and Affiliations

  1. Department of Geodesy and Surveying Engineering, Tafresh University, 79611-39518, Tafresh, Iran

    Alireza Safdarinezhad & Atiyeh Ganjali

Authors
  1. Alireza Safdarinezhad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Atiyeh Ganjali
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alireza Safdarinezhad.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Safdarinezhad, A., Ganjali, A. A least-squares method for simultaneous synchronization and relative calibration of overlapped videos. SIViP 17, 191–197 (2023). https://doi.org/10.1007/s11760-022-02221-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02221-3

Keywords

Search

Navigation