A 2.5D Approach to 360 Panorama Video Stabilization

Shen, Lin-Chen; Huang, Tzu-Kuei; Chen, Chu-Song; Chuang, Yung-Yu

doi:10.1109/icip.2018.8451037

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Their method incorporates four modules: pyramid-based motion detection, 2.5D motion parameter estimation, inertial motion analysis, and motion compensation. Shen et al [32] presented a 2.5D approach for stabilizing 360 • panorama videos. Their method decouples the rotational motion from other motions and handles them separately for improved stabilization outcomes.…”

Section: Related Workmentioning

confidence: 99%

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DMCVS: Decomposed motion compensation‐based video stabilization

Huang,

Liu,

Jiang

et al. 2024

IET Image Processing

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With the popularity of handheld devices, video stabilization is becoming increasingly important. In previous studies, many methods have been proposed to stabilize shaky videos. However, these methods fail to balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability. This work innovatively proposes a video stabilization method based on decomposed motion compensation. First, a grid‐based motion statistics method is adopted for motion estimation, which obtains more accurate motion vectors according to matched likelihood estimates. Then, the motion compensation is inherently decomposed into two parts: linear motion compensation and auxiliary motion compensation. Linear motion compensation removes complex jitter by constructing linear path constraints to obtain a more stable camera path. Auxiliary motion compensation uses a moving average filter to remove the high‐frequency jitter as a supplement and preserve more image content. The two components are combined with individual weights to derive the final transform matrix and warp the original frames. Experimental results show that our method outperforms the previous methods on NUS and DeepStab datasets qualitatively and quantitatively.

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Section: Related Workmentioning

confidence: 99%

“…Shen et al. [32] presented a 2.5D approach for stabilizing 360

^{\circ }

panorama videos. Their method decouples the rotational motion from other motions and handles them separately for improved stabilization outcomes.…”

Section: Related Workmentioning

confidence: 99%

DMCVS: Decomposed motion compensation‐based video stabilization

Huang,

Liu,

Jiang

et al. 2024

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…In [21], the authors through a 2.5D approach first estimate and remove all rotations; then, they employ mesh-based image warping [22] in order to compensate the remaining high-frequency jitters caused by camera translation and parallax. After that, they restore proper camera rotations.…”

Section: Related Workmentioning

confidence: 99%

“…This is possible if the positions and intentional rotations of the camera are fixed or almost fixed in time. In this case, for each frame, we could go back to the orientations of the first frame [21]. The rotation matrix applicable to each consecutive frame is a cumulative rotation matrix that sums all the contributions of the previ-Fig.…”

Section: Step 1: Estimating Relative Camera Rotationsmentioning

confidence: 99%

Stabilization of spherical videos based on feature uncertainty

et al. 2022

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Nowadays the trend is to acquire and share information in an immersive and natural way with new technologies such as Virtual Reality (VR) and 360$$^{\circ }$$ ∘ video. However, the use of 360$$^{\circ }$$ ∘ video, even more the use of VR head-mounted display, can generate general discomfort (“cybersickness”) and one factor is the video shaking. In this work, we developed a method to make the viewing of 360$$^{\circ }$$ ∘ video smoother and more comfortable to watch. First, the rotations are obtained with an innovative technique using a Particle Swarm Optimization algorithm considering the uncertainty estimation among features. In addition, a modified Chauvenet criterion is used to find and suppress outliers features from the algorithm. Afterward, a time-weighted color filter is applied to each frame in order to handle also videos with small translational jitter, rolling shutter wobble, parallax, and lens deformation. Thanks to our complete offline stabilization process, we achieved good-quality results in terms of video stabilization. Achieving better robustness compared to other works. The method was validated using virtual and real 360$$^{\circ }$$ ∘ video data of a mine environment acquired by a drone. Finally, a user study based on a subjective and standard Simulator Sickness Questionnaire was submitted to quantify simulator sickness before and after the stabilization process. The questionnaire underlined alleviation of cybersickness using stabilized videos with our approach

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