Feature-based Video Stabilization using Gabor Wavelets

Wan Nural Jawahir Hj Wan Yussof, Muhammad Suzuri Hitam, Abdul Aziz K. Abdul Hamid, Ezmahamrul Afreen Awalludin


This study proposes a method to stabilize jittery video using a feature-based technique. Our feature-based technique extracts local image features using Gabor wavelets. Firstly, to locate a set of interest points within a video frame, we detect some local maxima on Gabor response map image. Then, using the same Gabor response map image, we compute relational features around these interest points. The method was tested using shaky car video obtained from MATLAB version 2011b and compared with the SIFT and SURF methods. The output of using the proposed local image features is comparable to the output produced by SIFT and SURF methods and has shown good result concerning stabilization and discarded distortion from the output video.


Gabor Wavelets; Local Image Features; Relational Features; Video Stabilization;

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G. Spampinato, A. R. Bruna, I. Guarneri, and V. Tomaselli, “Advanced feature based digital video stabilization,” in 6th International Conference on IEEE Consumer Electronics-Berlin (ICCE-Berlin), 2016, pp. 54-56.

S. Battiato, G. Gallo, G. Puglisi, and S. Scellato, “SIFT features tracking for video stabilization,” in 14th International Conference on Image Analysis and Processing, ICIAP 2007, 2007, pp. 825-830.

C. Song, H. Zhao, W. Jing, and H. Zhu, “Robust video stabilization based on particle filtering with weighted feature points,” IEEE Transactions on Consumer Electronics, vol. 58, no. 2, pp. 570-577, May 2012.

J. Xu, H. W. Chang, S. Yang, and M. Wang, “Fast feature-based video stabilization without accumulative global motion estimation,” IEEE Transactions on Consumer Electronics, vol. 58, no 3, pp. 993-999, 2012

M. Okade and P. K. Biswas, “Video stabilization using maximally stable extremal region features,” Multimedia Tools and Applications, vol. 68, no. 3, pp.947-968, 2014.

A. Walha, A. Wali, and A. M. Alimi, “Video stabilization for aerial video surveillance,” AASRI Procedia, vol. 4, pp.72-77, 2013.

X. Zheng, C. Shaohui, W. Gang, and L. Jinlun, “Video stabilization system based on speeded-up robust features,” in Proc. Int. Industrial Informatics and Computer Engineering Conf., 2015, pp. 1996-1998.

M. M. Hossain, H. J. Lee, and J. Lee, “Fast image stitching for video stabilization using sift feature points,” The Journal of Korea Information and Communication Society, vol. 39, no. 10, pp.957-966, 2014.

Y. H. Chen, H. Y. S. Lin, and C. W. Su, “Full-frame video stabilization via SIFT feature matching,” in 2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIHMSP), 2014, pp. 361-364.

D. G. Lowe, “Distinctive Image features from scale-invariant keypoints” Int. Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.

H. Bay, T. Tuytelaars, and L. Gool, “Surf: Speeded up robust features,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346- 359, 2008.

Z. Chai, Z. Sun, H. Mendez-Vazquez, R. He, and T. Tan, “Gabor ordinal measures for face recognition,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 1, pp.14-26, 2014.

F. Riaz, A. Hassan, S. Rehman, and U. Qamar, “Texture classification using rotation and scale-invariant Gabor texture features,” IEEE Signal Processing Letters, vol. 20, no. 6, pp. 607-610, 2013.

Qian, Y., M. Ye, and J. Zhou, “Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 4, pp.2276-2291, 2013.

S. Agarwal, A. K. Verma, and N. Dixit, “Content-based image retrieval using color edge detection and discrete wavelet transform,” in 2014 International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014, pp. 368-372.

M. Schael, “Texture defect detection using invariant textural features,” in Joint Pattern Recognition Symposium, Berlin Heidelberg: Springer, 2001, pp. 17-24.

M.A. Fischler, and R.C.Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM, vol. 24, no. 6, pp. 381-395, 1981.

G. Shi, X. Xu, and Y. Dai, “SIFT feature point matching based on improved RANSAC algorithm,” in 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013, pp. 474-477.


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