Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time imag...

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Published in:	Electronics (Basel) Vol. 9; no. 1; p. 198
Main Authors:	Deng, Zilong, Yang, Dongxiao, Zhang, Xiaohu, Dong, Yuguang, Liu, Chengbo, Shen, Qiang
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.01.2020
Subjects:	Accuracy Algorithms Cameras Field programmable gate arrays Image quality Image stabilizers Kalman filters Matching Missile control Optical flow (image analysis) Real time Signal processing Stabilization Unmanned aerial vehicles
ISSN:	2079-9292, 2079-9292
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Abstract	The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.
AbstractList	The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.
Author	Shen, Qiang Zhang, Xiaohu Deng, Zilong Dong, Yuguang Liu, Chengbo Yang, Dongxiao
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Copyright_xml	– notice: 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms	Accuracy Algorithms Cameras Field programmable gate arrays Image quality Image stabilizers Kalman filters Matching Missile control Optical flow (image analysis) Real time Signal processing Stabilization Unmanned aerial vehicles
Title	Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching
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