The Image Stabilization Algorithms Testing System

Authors: Gavrilov D.A., Ivkin A.V., Shchelkunov N.N. Published: 07.12.2018
Published in issue: #6(123)/2018  
DOI: 10.18698/0236-3933-2018-6-22-36

Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing  
Keywords: algorithms of stabilization, computer vision, processing of video images

The study introduces a system for testing image stabilization algorithms. The analysis of input data is performed, ways of representing possible distortions are considered, algorithms for further tests are selected. A test bench was developed to test each algorithm and evaluate its applicability in the task of combining frames. The system for testing image stabilization algorithms consists of a parameterized video sequence generator that simulates a tracking mode, a parameterized distortion and interference generator, an error analysis algorithm for the alignment algorithm, and a report generation module. In the parameterized distortion generator, a physically correct noise model is implemented, as well as a model of interference in the image. The critical values of the distortion parameters of each tested algorithm are established experimentally. The testing and comparison of the selected algorithms was carried out. On the basis of the analysis of the obtained results, the most suitable algorithm was chosen for search of the combination of frames in real time


[1] Golovastov A. Machine vision and digital emage processing. Sovremennye tekhnologii avtomatizatsii [Contemporary Technologies in Automation], 2010, no. 2, pp. 8–18 (in Russ.).

[2] Vishnyakov B.V., Vizilter Yu.V., Lagutenkov A.V. Using modified optical flow method in problem of moving objects detecting and interframe tracking. Vestnik kompyuternykh i informatsionnykh tekhnologiy [Herald of Computer and Informational Technologies], 2007, no. 5, pp. 2–8 (in Russ.).

[3] Bronshteyn I.G., Unchun Ch., Starchenko A.P. Videocamera electronic stabilization image system research and methods development. Informatsionno-upravlyayushchie sistemy [Information and Control Systems], 2008, no. 1, pp. 7–11 (in Russ.).

[4] Karpukhin I.V. Methods of image stabilization. Evraziyskiy nauchnyy zhurnal, 2016, no. 2, pp. 174–177 (in Russ.).

[5] Borisova A.Yu., Smal A.V., Sinitsin A.V. Review and assessment of stabilization methods of machine vision systems. Molodezhnyy nauchno-tekhnicheskiy vestnik, 2015, no. 8 (in Russ.).

[6] Dumchev S.V., Dumchev E.V. Image stabilization is obtained with a statically set the camcorder. Izvestiya TulGU. Tekhnicheskie nauki [News of the Tula State University. Technical sciences], 2014, no. 9 (P. 1), pp. 64–70 (in Russ.).

[7] Kurchanov G.O., Shvedov A.P. Digital image stabilization system based on the method of extreme-correlation search poiska. Izvestiya TulGU. Tekhnicheskie nauki [News of the Tula State University. Technical Sciences], 2016, no. 10, pp. 103–111 (in Russ.).

[8] Zhu J., Guo B. Video stabilization with sub-image phase correlation. Chinese Opt. Lett., 2006, vol. 4, no. 9, pp. 553–555.

[9] Buryachenko V.V., Zotin A.G. Analysis of video stabilization methods based on frame motion detection. Aktualnye problemy aviatsii i kosmonavtiki [Actual Problems of Aviation and Cosmonautics], 2010, no. 6, pp. 339–341 (in Russ.).

[10] Shcherbakov V.V., Garganeev A.G., Shakirov I.V. The algorithm for calculating the optical flow estimation in the tasks of the parameters of geometric transformations. Doklady TUSUR [Proceedings of TUSUR], 2012, no. 2 (26), p. 1, pp. 265–268 (in Russ.).

[11] Bab–Hadiashar A., Suter D. Robust optic flow computation. Int. J. Comput. Vis., 1998, vol. 29, no. 1, pp. 59–77. DOI: 10.1023/A:1008090730467

[12] Strelnikov K.N. Using the features of the modern presentation of digital video processing algorithms for optimization. Tekhnicheskoe zrenie v sistemakh upravleniya mobilnymi obektami–2010. Trudy nauch.-tekh. konf.-seminara [Computer vision in control systems of the mobile objects–2010. Proc. of the Conf.-Workshop]. Moscow, KDU Publ., 2011, no. 4, pp. 277–281 (in Russ.).

[13] Hartley R., Zisserman A. Multiple view geometry in computer vision. New York, Cambridge University Press, 2004. 670 p.

[14] Lowe D. Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis., 2004, no. 1, pp. 91–110. DOI: 10.1023/B:VISI.0000029664.99615.94

[15] Bay H., Essa A., Tuytelaars T., Van Gool L. Speeded-up robust features (SURF). Comput. Vis. Image Underst., 2008, vol. 110, no. 3, pp. 346–359. DOI: 10.1016/j.cviu.2007.09.014

[16] Szeliski R. Image alignment and stitching: a tutorial. Foundations and Trends in Computer Graphics and Vision, 2006, vol. 2, no. 1, pp. 1–104.