Electronic Image Stabilization System for Video Cameras and VCRs

Uomori, Kenya; Morimura, Atsushi; Ishii, Hirofumi

doi:10.5594/j02339

Cited by 27 publications

(15 citation statements)

References 2 publications

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“…In recent years videos acquired by cameras mounted on a small UAV, with timeliness and flexibility, have got wide application prospects in tactical reconnaissance, surveillance and many other applications [1] [2] . However, due to the complexity of UAV wobble and impacts of surroundings, the videos are always unstable, leading to the airborne video streams with wobbly and shaking motions, disorienting rotations, noisy and other unwanted movements， which often cause the observer feeling vertigo and visual fatigue, even misjudgment.…”

Section: Introductionmentioning

confidence: 99%

Intended motion estimation using fuzzy Kalman filtering for UAV image stabilization with large drifting

et al. 2015

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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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Section: Introductionmentioning

confidence: 99%

Intended motion estimation using fuzzy Kalman filtering for UAV image stabilization with large drifting

et al. 2015

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“…However, all these feature based methods have reported a drawback when the input video frames are severely blurred. In the motion smoothing stage techniques like motion vector integration [10,11], frame position smoothing [12], Gaussian filtering [13], Kalman filtering [14], and extended Kalman filtering [15] have been used to separate out the desired motion from the undesired motion. In practice, the stabilized video images often do not overlap perfectly with the boundary of the desired image frame due to motion compensation operation and as a result missing regions are formed along with impairment in resolution.…”

Section: Introductionmentioning

confidence: 99%

Improving Video Stabilization Using Multi-Resolution MSER Features

Okade

Biswas

2014

IETE Journal of Research

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In this paper, we investigate the application of multi-resolution maximally stable extremal region (MSER) features for improving the video stabilization performance. MSER features have been used for many computer vision applications like wide baseline stereo, object recognition, video object tracking, and video stabilization with very good results as compared to other features like scale invariant feature transform (SIFT) and Kanade Lucas Tomasi (KLT). However, a limitation of the MSER feature in the stabilization application was observed when the input video frames were severely blurred. The same limitation was also observed when other features like KLT and SIFT were utilized under blurring conditions. In this paper we propose to overcome this drawback for video stabilization application by utilizing MSERs which are extracted and matched in a scale pyramid fashion instead of the MSER features detected and matched on a single image resolution. The duplicate MSERs resulting due to the pyramid style detection are removed followed by MSER feature matching for establishing correspondence between video frames to estimate the global motion parameters. Once the global motion parameters are estimated, the accumulated transformation is smoothed followed by motion compensation to construct the stabilized frame. Comparative analysis with state-of-the-art stabilization methods shows improvement in stabilization performance as well as robustness to blurring degradations. The proposed method can easily be ported to other feature detectors like KLT and SIFT thereby making the proposed method generic to any feature detector.

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“…In [4], global motion estimation using local motion vectors of subimages obtained by block-matching has been presented. Motion estimation based on edge pattern matching has been demonstrated in [5].…”

Section: Introductionmentioning

confidence: 99%

“…Two main techniques have been proposed, so far, for the stabilization of translational fluctuations: motion vector integration (MVI) [3,4,8] and DFT filtered frame position smoothing (DFT-FPS) [10]. Motion vector integration constitutes a first-order low-pass infinite impulse response (IIR) filter that integrates differential interframe motion vectors to smoothen the global movement trajectory by simple real-time operation.…”

Section: Introductionmentioning

confidence: 99%