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• W4313017969 abstract "Multicamera calibration is an important technique for generating free-view videos. By arranging multiple cameras in a scene after camera calibration and image processing, a multidimensional viewing experience can be presented to the audience. To address the problem that low texture cannot be robustly self-calibrated in common sports scenes when placing artificial markers or towers in the calibration process is impractical, this article proposes a robust multicamera calibration method based on sequence feature matching and fusion. Additionally, to validate the effectiveness of the proposed calibration algorithm, a virtual axis fast bullet-time synthesis algorithm is proposed for generating a free-view video. First, camera self-calibration is performed in low-texture situations by fusing dynamic objects in time series to enrich geometric constraints in scenes without the use of calibration panels or additional artificial markers. Second, a virtual-axis bullet-time video synthesis method based on the calibration result is proposed. In the calibrated multicamera scenario, a fast bullet-time video is generated by constructing a virtual axis. Qualitative and quantitative experiments in comparison with a state-of-the-art calibration method demonstrate the validity and robustness of the proposed calibration algorithm for free-view video synthesis tasks." @default.
• W4313017969 created "2023-01-05" @default.
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• W4313017969 date "2023-02-01" @default.
• W4313017969 modified "2023-10-17" @default.
• W4313017969 title "Time-Series Fusion-Based Multicamera Self-Calibration for Free-View Video Generation in Low-Texture Sports Scene" @default.
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• W4313017969 doi "https://doi.org/10.1109/jsen.2022.3230792" @default.
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