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Constructing big panorama from video sequence based on deep local feature
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DOI：10.1016/j.imavis.2020.103972.png)
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Constructing high-quality panorama is a fundamental task in both computer vision and computer graphics communities, thus, leading to many image stitching approaches for panorama construction. However, panorama constructed by traditional image stitching has a limited angle of view and has an expensively computational cost. To defend the issues, in this paper, we proposed to use video sequence as input for constructing big panorama, resulting in a high-quality panoramic image, the presented method is built on the stabilized video and robust feature tracking method. Specifically, the input video sequences are captured by moving hand cameras which can be any type of consumer-level camera. To mitigate the affections from rolling shutters in videos, a novel video stabilization method is introduced to filter the unstable camera's path, then resulting in a stabilized video for panorama construction. Additionally, a deep local feature-based feature tracking method is proposed to produce feature correspondences between consecutive video frames for camera motion estimation used in both video stabilization and image stitching. Finally, a comprehensive experiment conducted on the benchmarking datasets is presented to demonstrate the effectiveness of the proposed method. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Panorama
Deep local feature
Feature tracking
Image stitching
Image blending
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