Paper
16 March 2015 Intended motion estimation using fuzzy Kalman filtering for UAV image stabilization with large drifting
Tiantian Xin, Hongying Zhao, Sijie Liu, Lu Wang
Author Affiliations +
Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 939914 (2015) https://doi.org/10.1117/12.2083102
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States
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.
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Tiantian Xin, Hongying Zhao, Sijie Liu, and Lu Wang "Intended motion estimation using fuzzy Kalman filtering for UAV image stabilization with large drifting", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 939914 (16 March 2015); https://doi.org/10.1117/12.2083102
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KEYWORDS
Filtering (signal processing)

Electronic filtering

Motion estimation

Image filtering

Unmanned aerial vehicles

Video

Fuzzy logic

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