Online Digital Image Stabilization for an Unmanned Aerial Vehicle (UAV)

Wahyu Rahmaniar, Amalia Eka Rakhmania

Abstract


The Unmanned Aerial Vehicle (UAV) video system uses a portable camera mounted on the robot to monitor scene activities. In general, UAVs have very little stabilization equipment, so getting good and stable images of UAVs in real-time is still a challenge. This paper presents a novel framework for digital image stabilization for online applications using a UAV. This idea aims to solve the problem of unwanted vibration and motion when recording video using a UAV. The proposed method is based on dense optical flow to select features representing the displacement of two consecutive frames. K-means clustering is used to find the cluster of the motion vector field that has the largest members. The centroid of the largest cluster was chosen to estimate the rigid transform motion that handles rotation and translation. Then, the trajectory is compensated using the Kalman filter. The experimental results show that the proposed method is suitable for online video stabilization and achieves an average computation time performance of 47.5 frames per second (fps).

Keywords


K-means; Kalman filter; image stabilization; optical flow; UAV

Full Text:

PDF

References


Z. Zheng, Y. Ma, H. Zheng, Y. Gu, and M. Lin, “Industrial part localization and grasping using a robotic arm guided by 2D monocular vision,” Ind. Rob., vol. 45, no. 6, pp. 794–804.

B. N. Vahrenkamp and T. Asfour, “Simultaneous grasp and motion planning: Humanoid robot ARMAR-III,” IEEE Robotics and Automation Magazine, vol. 19, no. 2, pp. 43–57, 2012.

W. Rahmaniar and A. Wicaksono, “Design and Implementation of a Mobile Robot for Carbon Monoxide Monitoring,” J. Robot. Control, vol. 2, no. 1, 2021.

M. Dunbabin and L. Marques, “Robots for environmental monitoring: Significant advancements and applications,” IEEE Robot. Autom. Mag., vol. 19, no. 1, pp. 24–39, 2012.

W. Rahmaniar, W. Wang, and H. Chen, “Real-time detection and recognition of multiple moving objects for aerial surveillance,” Electronics, vol. 8, no. 12, pp. 1373-1390, 2019.

W. C. Hu, C. H. Chen, T. Y. Chen, D. Y. Huang, and Z. C. Wu, “Moving object detection and tracking from video captured by moving camera,” J. Vis. Commun. Image Represent., vol. 30, pp. 164–180, 2015.

Y. Lu, Z. Xue, G.-S. Xia, and L. Zhang, “A survey on vision-based UAV navigation,” Geo-spatial Inf. Sci., vol. 21, no. 1, pp. 21–32, 2018.

S. Minaeian, J. Liu, and Y.-J. Son, “Vision-based target detection and localization via a team of cooperative UAV and UGVs,” IEEE Trans. Syst. Man, Cybern. Syst., vol. 46, no. 7, pp. 1005–1016, Jul. 2016.

D. H. Yeom, “Optical image stabilizer for digital photographing apparatus,” IEEE Trans. Consum. Electron., vol. 55, no. 3, pp. 1028–1031, 2009.

P. Pournazari, R. Nagamune, and M. Chiao, “A concept of a magnetically-actuated optical image stabilizer for mobile applications,” IEEE Trans. Consum. Electron., vol. 60, no. 1, pp. 10–17, 2014.

M. Hashimoto, T. Kuno, and H. Sugiura, “A new image-stabilizing method by transferring electric charges,” in Proc. of Digest of Technical Papers - IEEE International Conference on Consumer Electronics, 2007, pp. 1-2.

C. Song, H. Zhao, W. Jing, and H. Zhu, “Robust video stabilization based on particle filtering with weighted feature points,” IEEE Trans. Consum. Electron., vol. 58, no. 2, pp. 570–577, 2012.

J. H. Woo, J. H. Yoon, Y. J. Hur, N. C. Park, Y. P. Park, and K. S. Park, “Optimal design of a ferromagnetic yoke for reducing crosstalk in optical image stabilization actuators,” IEEE Transactions on Magnetics, 2011, vol. 47, no. 10, pp. 4298–4301.

C. Kim, M. G. Song, Y. Kim, N. C. Park, K. S. Park, and Y. P. Park, “Design of a new triple electro-magnetic optical image stabilization actuator to compensate for hand trembling,” Microsyst. Technol., vol. 18, no. 9–10, pp. 1323–1334, 2012.

T. Kinugasa, N. Yamamoto, H. Komatsu, S. Takase, and T. Imaide, “Electronic image stabilizer for video camera use,” IEEE Trans. Consum. Electron., vol. 36, no. 3, pp. 520–525, 1990.

Y. Zhang and M. Xie, “Robust digital image stabilization technique for car camera,” Inf. Technol. J., vol. 10, no. 2, pp. 335–347, 2011.

L. Kejriwal and I. Singh, “A hybrid filtering approach of digital video stabilization for UAV using kalman and low pass filter,” Procedia Comput. Sci., vol. 93, no. September, pp. 359–366, 2016.

J. Li, T. Xu, and K. Zhang, “Real-time feature-based video stabilization on FPGA,” IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 4, pp. 907–919, 2017.

B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proc. of International Joint Conference on Artificial Intelligence, 1981, pp. 674--679.

A. Lim, B. Ramesh, Y. Yang, C. Xiang, Z. Gao, and F. Lin, “Real-time optical flow-based video stabilization for unmanned aerial vehicles,” J. Real-Time Image Process., pp. 1–11, 2017.

C. Feichtenhofer and A. Pinz, “Good features to track,” in Proc. of IEEE International Conference on Computer Vision, 1994, pp. 246–253.

Y. G. Ryu and M. J. Chung, “Robust online digital image stabilization based on point-feature trajectory without accumulative global motion estimation,” IEEE Signal Process. Lett., vol. 19, no. 4, pp. 223–226, 2012.

C. Wang, J. Kim, K. Byun, J. Ni, and S. Ko, “Robust digital image stabilization using the Kalman filter,” IEEE Trans. Consum. Electron., vol. 55, no. 1, pp. 6–14, 2009.

X. Wang, X. He, Q. Teng, and M. Gao, “Digital image stabilization based on harmony filter,” J. Softw., vol. 9, no. 4, pp. 913–920, 2014.

W. G. Aguilar and C. Angulo, “Real-Time model-based video stabilization for microaerial vehicles,” Neural Process. Lett., vol. 43, no. 2, pp. 459–477, 2016.

T. Yabuki and Y. Yamaguchi, “Real-time video stabilization on an FPGA,” in Proc. of 2013 IEEE International Conference on Smart Structures and Systems, 2013, pp. 114–119.

S. Kumar, H. Azartash, M. Biswas, and T. Nguyen, “Real-time affine global motion estimation using phase correlation and its application for digital image stabilization.,” IEEE Trans. image Process., vol. 20, no. 12, pp. 3406–18, 2011.

G. Liu, T. Wang, L. Yu, Y. Li, and J. Gao, “The improved research on K-means clustering algorithm in initial values,” in Proc. of Int. Conf. Mechatron. Sci. Electr. Eng. Comput., 2013, pp. 2124–2127, 2013.

R. Tibshirani, G. Walther, and T. Hastie, “Estimating the number of clusters in a data set via the gap statistic,” J. R. Stat. Soc. Ser. B Stat. Methodol., vol. 63, no. 2, pp. 411–423, 2001.

G. Farnebäck, “Two-frame motion estimation based on polynomial expansion,” in Proc. of Scandinavian Conference on Image Analysis, 2003, pp. 363–370.




DOI: https://doi.org/10.18196/jrc.2484

Article Metrics

Abstract view : 146 times
PDF - 84 times

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Journal of Robotics and Control (JRC)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.


Abstracted/Indexed by:

    

    

 


Journal of Robotics and Controls (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Lembaga Penelitian, Publikasi & Pengabdian Masyarakat UMY, Yogyakarta, Indonesia
Published by Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrc@umy.ac.id || jrcofumy@gmail.com


 

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.