Capturing Intention‐based Full‐Frame Video Stabilization

Chen, Bing‐Yu; Lee, Ken‐Yi; Huang, Weiting; Lin, Jong‐Shan

doi:10.1111/j.1467-8659.2008.01326.x

Cited by 62 publications

(48 citation statements)

References 19 publications

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“…To stabilize the video clips by using methods such as [16], [18] before segmentation may be a possible solution to the problem. It is also difficult if the foreground object has complex or irregular motion patterns, such like a rapid swimming goldfish.…”

Section: Discussionmentioning

confidence: 99%

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Video Segmentation with Motion Smoothness

Wen

Chen

Sato

2010

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we present an interactive and intuitive graphcut-based video segmentation system while taking both color and motion information into consideration with a stroke-based user interface. Recently, graph-cut-based methods become prevalent for image and video segmentation. However, most of them deal with color information only and usually failed under circumstances where there are some regions in both foreground and background with similar colors. Unfortunately, it is usually hard to avoid, especially when the objects are filmed under a natural environment. To make such methods more practical to use, we propose a graph-cut-based video segmentation method based on both color and motion information, since the foreground objects and the background usually have different motion patterns. Moreover, to make the refinement mechanism easy to use, the strokes drawn by the user are propagated to the temporal-spatial video volume according to the motion information for visualization, so that the user can draw some additional strokes to refine the segmentation result in the video volume. The experiment results show that by combining both color and motion information, our system can resolve the wrong labeling due to the color similarity, even the foreground moving object is behind an occlusion object.

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Section: Discussionmentioning

confidence: 99%

“…However, since the raw optical flow still has some noises, we further conduct color-guided weighting average in the fashion similar to [16]:…”

Section: The Motion Smoothness Termmentioning

confidence: 99%

Video Segmentation with Motion Smoothness

Wen

Chen

Sato

2010

IEICE Trans. Inf. & Syst.

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“…Two-dimensional stabilization methods estimate a homography sequence from the input video, dampen this sequence, and warp video frames based on the smoothed sequence [9], [10], [11], [12], [13], [14]. A recent method achieves aggressive smoothing using L1 optimization [8], in which the gradient of the camera motion at each frame can be strongly suppressed, to produce very high-quality results.…”

Section: Related Workmentioning

confidence: 99%

Spatially and Temporally Optimized Video Stabilization

Wang

Liu

Hsu

et al. 2013

IEEE Trans. Visual. Comput. Graphics

101

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Abstract-Properly handling parallax is important for video stabilization. Existing methods that achieve the aim require either 3D reconstruction or long feature trajectories to enforce the subspace or epipolar geometry constraints. In this paper, we present a robust and efficient technique that works on general videos. It achieves high-quality camera motion on videos where 3D reconstruction is difficult or long feature trajectories are not available. We represent each trajectory as a Bé zier curve and maintain the spatial relations between trajectories by preserving the original offsets of neighboring curves. Our technique formulates stabilization as a spatialtemporal optimization problem that finds smooth feature trajectories and avoids visual distortion. The Bé zier representation enables strong smoothness of each feature trajectory and reduces the number of variables in the optimization problem. We also stabilize videos in a streaming fashion to achieve scalability. The experiments show that our technique achieves high-quality camera motion on a variety of challenging videos that are difficult for existing methods.

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“…Mechanical stabilization physically dampens out vibration or unintended movement with gyroscopes. Optical stabilization modifies each input frame to maintain a steady image after it is converted to digital form [2]. Generally the processes of stabilization goes through three phases namely 1) Motion estimation 2) Motion smoothing 3) Motion compensation.…”

Section: Introductionmentioning

confidence: 99%

Stabilization of Jittery Videos Using Feature Point Matching Technique

Kulkarni

Bormane

Nalbalwar

2017

Proceedings of the International Conference on Communication and Signal Processing 2016 (ICCASP 2016)

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Abstract:Video capturing by non-professionals will lead to unanticipated effects. such as image distortion, image blurring etc. Many researchers focused on these drawbacks to enhance the quality of videos. In this paper an algorithm based on s-R-t transform is proposed to stabilize jittery videos .A stable output video is obtained without the effect of jitter which is caused due to shaking of handheld camera during video recording. In this technique firstly, salient points from each frame from the input video are identified using FAST algorithm. Camera motion is corrected by affine transform and motion compensation is performed by s-R-t transform which gives stabilized video. Optimization includes the quality of the video stabilization. This method has shown good result in terms of stabilization and it discarded distortion from the output videos recorded in different circumstances.

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Capturing Intention‐based Full‐Frame Video Stabilization

Abstract: Annoying shaky motion is one of the significant problems in home videos

Cited by 62 publications

References 19 publications

Video Segmentation with Motion Smoothness

Video Segmentation with Motion Smoothness

Spatially and Temporally Optimized Video Stabilization

Stabilization of Jittery Videos Using Feature Point Matching Technique

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