Skip to main content
SpringerLink
Log in

Real-time video stabilization for fast-moving vehicle cameras

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Most previous methods of real-time video stabilization are only effective for low-vibrating frames which are usually captured by in-vehicle camera at the low-speed moving. To overcome their ineffectiveness on high-vibrating frames, this paper presents a real-time video stabilization system for the video sequences captured by a fast-moving in-vehicle camera without additional sensors. The proposed method is composed of four parts: frame-shaking judgment, feature classification, evaluating global motion and rotation angle, and frame compensation. Feature points and their motion vectors are employed for judging whether the current frame is shaking or not, and then a conversion matrix is deduced through the perspective projection for classifying such feature points into background or foreground type. Next, the optical flows of background’s feature points are mapped to polar coordinates for obtaining the representative optical-flow cluster of the background. Finally, such a cluster is utilized to calculate the global motion and rotation angle for compensation followed by the Kalman filtering in order to provide the better video stabilization. Experimental results show that the proposed method has good real-time video stabilization for a vehicle camera moving at various speeds and better stabilization performance than other methods for high-vibrating frames when both real-time processing and acceptable stabilization result are considered.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Aguilar WG, Angulo C (2014) Real-time video stabilization without phantom movements for micro aerial vehicles. EURASIP Journal on Image and Video Processing 1: 46:1–46:13

  2. Chen C-H, Chen T-Y, Hu W-C, Peng M-Y (2015) Video stabilization for fast moving camera based on feature point classification. In: Proc. of the Third International Conference on Robot, Vision and Signal Processing, pp. 10–13

  3. Choi W, Pantofaru C, Savarese S (2013) A general framework for tracking multiple people from a moving camera. IEEE Trans Pattern Anal Mach Intell 35(7):1577–1591

    Article  Google Scholar 

  4. Deshaker method. Available from: http://www.guthspot.se/video/deshaker.htm

  5. Duda RO, Hart PE (1972) Use of the Hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15

    Article  MATH  Google Scholar 

  6. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proc. of the Second International Conference on Knowledge Discovery and Data Mining, pp. 226–231

  7. Harris C, Stephens M (1988) A combined corner and edge detector. In: Proc. of the Fourth Alvey Vision Conference, pp. 147–151

  8. Hu W-C (2011) Real-time on-line video object segmentation based on motion detection without background construction. International Journal of Innovative Computing, Information and Control 7(4):1845–1860

    Google Scholar 

  9. Hu W-C, Yang C-Y, Huang D-Y (2011) Robust real-time ship detection and tracking for visual surveillance of cage aquaculture. J Vis Commun Image Represent 22(6):543–556

    Article  Google Scholar 

  10. Hu W-C, Chen C-H, Chen C-M, Chen T-Y (2014) Effective moving object detection from videos captured by a moving camera. In: Proc. of the First Euro-China Conference on Intelligent Data Analysis and Applications 1:343–353

  11. Hu W-C, Chen C-H, Chen T-Y, Huang D-Y, Wu Z-C (2015) Moving object detection and tracking from video captured by moving camera. J Vis Commun Image Represent 30:164–180

    Article  Google Scholar 

  12. Huang D-Y, Chen C-H, Chen T-Y, Hu W-C, Lin Y-L (2016) A vehicle flow counting system in rainy environment based on vehicle feature analysis. Journal of Information Hiding and Multimedia Signal Processing 7(1):101–114

    Google Scholar 

  13. Jia C, Evans B (2014) Constrained 3D rotation smoothing via global manifold regression for video stabilization. IEEE Trans Signal Process 62(13):3293–3304

    Article  MathSciNet  Google Scholar 

  14. Kalman RE (1960) A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering 82:35–45

    Article  Google Scholar 

  15. Kim SK, Kang SJ, Wang TS, Ko SJ (2013) Feature point classification based global motion estimation for video stabilization. IEEE Trans Consum Electron 59(1):267–272

    Article  Google Scholar 

  16. Liang YM, Tyan HR, Chang SL, Liao HYM, Chen SW (2004) Video stabilization for a camcorder mounted on a moving vehicle. IEEE Trans Veh Technol 53(6):1636–1648

    Article  Google Scholar 

  17. Licsár A, Czúni L, Szirányi T (2003) Adaptive stabilization of vibration on archive films. Lect Notes Comput Sci 2756:230–237

    Article  MathSciNet  Google Scholar 

  18. Liu F, Gleicher M, Jin H, Agarwala A (2009) Content-preserving warps for 3D video stabilization. ACM Trans Graph 28(3):44:1–44:9

    Google Scholar 

  19. Liu F, Gleicher M, Wang J, Jin H, Agarwala A (2011) Subspace video stabilization. ACM Trans Graph 30(1):4:1–4:10

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Lucas BD, Kanade T (1981) An iterative image registration technique with an application to stereo vision. In: Proc. of the 7th International Joint Conference on Artificial Intelligence, pp. 674–679

  22. Marcenaro L, Vernazza G, Regazzoni CS (2001) Image stabilization algorithm for video-surveillance applications. In: Proc. of the International Conference on Image Processing 1:349–352

  23. Nicolescu M, Medioni G (2005) A voting-based computational framework for visual motion analysis and interpretation. IEEE Trans Pattern Anal Mach Intell 27(5):739–758

    Article  Google Scholar 

  24. Shen Y, Guturu P, Damarla T, Buckles BP, Namuduri KR (2009) Video stabilization using principal component analysis and scale invariant feature transform in particle filter framework. IEEE Trans Consum Electron 55(3):1714–1721

    Article  Google Scholar 

  25. Shi J, Tomasi C (1994) Good features to track. In: Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 593–600

  26. Wang Y, Hou Z, Leman K, Chang R (2011) Real-time video stabilization for unmanned aerial vehicles. In: Proc. of the 12th IAPR Conference on Machine Vision Applications, pp. 336–339

  27. Wang Y-S, Liu F, Hsu P-S, Lee T-Y (2013) Spatially and temporally optimized video stabilization. IEEE Trans Vis Comput Graph 19(1):1354–1361

    Article  Google Scholar 

  28. Zhang Y, Xie M, Tang D (2010) A central sub-image based global motion estimation method for in-car video stabilization. In: Proc. of the IEEE Third International Conference on Knowledge Discovery and Data Mining, pp. 204–207

Download references

Acknowledgment

This work was partly supported by the Ministry of Science and Technology, Taiwan, under grants MOST105-2221-E-346-009, MOST104-2221-E-151-008, and MOST104-2622-E-151-015-CC3. The authors wish to express the appreciation to Mr. Jhih-Bin Guo and Prof. Tong-Yee Lee for their help with the experiments. The authors also gratefully acknowledge the helpful comments and suggestions of reviewers, which have improved the quality and presentation.

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Penghu University of Science and Technology, Penghu, Taiwan, Republic of China

    Wu-Chih Hu

  2. Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, Republic of China

    Chao-Ho Chen, Tsong-Yi Chen & Min-Yang Peng

  3. Department of Computer Science and Information Engineering, Shu-Te University, Kaohsiung, Taiwan, Republic of China

    Yi-Jen Su

Authors
  1. Wu-Chih Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chao-Ho Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tsong-Yi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Min-Yang Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yi-Jen Su
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chao-Ho Chen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, WC., Chen, CH., Chen, TY. et al. Real-time video stabilization for fast-moving vehicle cameras. Multimed Tools Appl 77, 1237–1260 (2018). https://doi.org/10.1007/s11042-016-4291-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-4291-4

Keywords

Search

Navigation