SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization

Shuaicheng Liu1         Lu yuan2         Ping Tan1         Jian Sun2

1. National University of Singapore               2. Microsoft Research


We propose a novel motion model, SteadyFlow, to represent the motion between neighboring video frames for stabilization. A SteadyFlow is a specific optical flow by enforcing strong spatial coherence, such that smoothing feature trajectories can be replaced by smoothing pixel profiles, which are motion vectors collected at the same pixel location in the SteadyFlow over time. In this way, we can avoid brittle feature tracking in a video stabilization system. Besides, SteadyFlow is a more general 2D motion model which can deal with spatially-variant motion. We initialize the SteadyFlow by optical flow and then discard discontinuous motions by a spatial-temporal analysis and fill in missing regions by motion completion. Our experiments demonstrate the effectiveness of our stabilization on real-world challenging videos.


Paper [PDF]

Related Projects

Shuaicheng Liu, Lu yuan, Ping Tan, Jian Sun. Bundled Camera Paths for Video Stabilization. ACM Transactions on Graphics (Proceeding of SIGGRAPH) 2013. [PDF][project page]

Shuaicheng Liu, Yinting Wang, Lu Yuan, Jiajun Bu, Ping Tan, Jian Sun: Video Stabilization with a Depth Camera. IEEE Conference on Computer Vision and Patten Recognition(CVPR) 2012 [PDF][project page]


Video Spotlight


Full demo Video: download [64Mb]




Example 1: Rolling shutter together with large occlusion
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Example 2: The synthesized example
Input   Our method Liu et al 2013
Example 3: Two rolling shutter examples
Input  Our method Baker et al.2010
Input Our method Karpenko et al 2011
Example 4: Videos contain large foreground , moving towards camera
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Example 5: Video contains quick camera zooming, comparison between with and without adaptive smoothing.
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Example 6: Stabilize by raw optical flow  
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Example 7: Motion completion by strong gaussian smoothing
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Example 8: more examples in the paper
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Our spatial-temporal analysis failed to distinguish foreground and background when videos contain dominate foregrounds, (foregrounds occupy more than half area of a frame and exist for a long time).

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