Improved optimal seam selection blending for fast video stitching of videos captured from freely moving devices

El-Saban, Motaz; Izz, Mostafa; Kaheel, Ayman; Refaat, Mahmoud

doi:10.1109/icip.2011.6115723

Cited by 23 publications

(7 citation statements)

References 17 publications

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“…This two-step approach has already been used in video synthesis commercial softwares such as Autopano [41]. The neighboring frames of a video are correlated, therefore, sufficient usage of previous frame could accelerate the stitching of current frame [42], [43]. Though the methods mentioned above could achieve real-time video stitching, little consideration has been given on the quality of stitching, especially from the algorithm point of view.…”

Section: B Video Stitchingmentioning

confidence: 99%

Efficient Video Stitching Based on Fast Structure Deformation

Zhang

et al. 2015

IEEE Trans. Cybern.

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In computer vision, video stitching is a very challenging problem. In this paper, we proposed an efficient and effective wide-view video stitching method based on fast structure deformation that is capable of simultaneously achieving quality stitching and computational efficiency. For a group of synchronized frames, firstly, an effective double-seam selection scheme is designed to search two distinct but structurally corresponding seams in the two original images. The seam location of the previous frame is further considered to preserve the interframe consistency. Secondly, along the double seams, 1-D feature detection and matching is performed to capture the structural relationship between the two adjacent views. Thirdly, after feature matching, we propose an efficient algorithm to linearly propagate the deformation vectors to eliminate structure misalignment. At last, image intensity misalignment is corrected by rapid gradient fusion based on the successive over relaxation iteration (SORI) solver. A principled solution to the initialization of the SORI significantly reduced the number of iterations required. We have compared favorably our method with seven state-of-the-art image and video stitching algorithms as well as traditional ones. Experimental results show that our method outperforms the existing ones compared in terms of overall stitching quality and computational efficiency.

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Section: B Video Stitchingmentioning

confidence: 99%

Efficient Video Stitching Based on Fast Structure Deformation

Zhang

et al. 2015

IEEE Trans. Cybern.

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“…To reduce processing time, some papers have proposed using additional information of the video. El-Saban [22] used the motion vector from SURF descriptor matching. Shimizu [23] also used the motion data contained in the information of compressed video streams.…”

Section: Video Stitchingmentioning

confidence: 99%

Real-Time Video Stitching Using Camera Path Estimation and Homography Refinement

Yoon

Lee

2017

Symmetry

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We propose a novel real-time video stitching method using camera path estimation and homography refinement. The method can stably stitch multiple frames acquired from moving cameras in real time. In the proposed method, one initial between-camera (BC) homography and each camera path (CP) homography are used to estimate the BC homography at every frame. The BC homography is refined by using block matching to adjust the errors of estimated CPs (homography refinement). For fast processing, we extract features using the difference of intensities and use the optical flow to estimate camera motion (CM) homographies, which are multiplied with the previous CMs to calculate CPs (camera path estimations). In experiments, we demonstrated the performance of the CP estimation and homography refinement approach by comparing it with other methods. The experimental results show that the proposed method can stably stitch two image sequences at a rate exceeding 13 fps (frames per second).

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“…Another related topic is the panoramic video [34][35][36][37][38]. For instance, Perazzi et al [35] create a panoramic video from an array of stationary cameras by generalizing parallax-tolerant image stitching to video stitching.…”

Section: Prior Workmentioning

confidence: 99%

Temporally Robust Global Motion Compensation by Keypoint-Based Congealing

Safdarnejad

Atoum

Liu

2016

Computer Vision – ECCV 2016

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Global motion compensation (GMC) removes the impact of camera motion and creates a video in which the background appears static over the progression of time. Various vision problems, such as human activity recognition, background reconstruction, and multi-object tracking can benefit from GMC. Existing GMC algorithms rely on sequentially processing consecutive frames, by estimating the transformation mapping the two frames, and obtaining a composite transformation to a global motion compensated coordinate. Sequential GMC suffers from temporal drift of frames from the accurate global coordinate, due to either error accumulation or sporadic failures of motion estimation at a few frames. We propose a temporally robust global motion compensation (TRGMC) algorithm which performs accurate and stable GMC, despite complicated and long-term camera motion. TRGMC densely connects pairs of frames, by matching local keypoints of each frame. A joint alignment of these frames is formulated as a novel keypoint-based congealing problem, where the transformation of each frame is updated iteratively, such that the spatial coordinates for the start and end points of matched keypoints are identical. Experimental results demonstrate that TRGMC has superior performance in a wide range of scenarios.

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Improved optimal seam selection blending for fast video stitching of videos captured from freely moving devices

Cited by 23 publications

References 17 publications

Efficient Video Stitching Based on Fast Structure Deformation

Efficient Video Stitching Based on Fast Structure Deformation

Real-Time Video Stitching Using Camera Path Estimation and Homography Refinement

Temporally Robust Global Motion Compensation by Keypoint-Based Congealing

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