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Proceedings Paper

Intended motion estimation using fuzzy Kalman filtering for UAV image stabilization with large drifting
Author(s): Tiantian Xin; Hongying Zhao; Sijie Liu; Lu Wang
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Paper Abstract

Videos from a small Unmanned Aerial Vehicle (UAV) are always unstable because of the wobble of the vehicle and the impact of surroundings, especially when the motion has a large drifting. Electronic image stabilization aims at removing the unwanted wobble and obtaining the stable video. Then estimation of intended motion, which represents the tendency of global motion, becomes the key to image stabilization. It is usually impossible for general methods of intended motion estimation to obtain stable intended motion remaining as much information of video images and getting a path as much close to the real flying path at the same time. This paper proposed a fuzzy Kalman filtering method to estimate the intended motion to solve these problems. Comparing with traditional methods, the fuzzy Kalman filtering method can achieve better effect to estimate the intended motion.

Paper Details

Date Published: 16 March 2015
PDF: 7 pages
Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939914 (16 March 2015); doi: 10.1117/12.2083102
Show Author Affiliations
Tiantian Xin, Peking Univ. (China)
Hongying Zhao, Peking Univ. (China)
Sijie Liu, Peking Univ. (China)
Lu Wang, Peking Univ. (China)

Published in SPIE Proceedings Vol. 9399:
Image Processing: Algorithms and Systems XIII
Karen O. Egiazarian; Sos S. Agaian; Atanas P. Gotchev, Editor(s)

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