Automatic video mosaicking is a challenging task in computer vision. Current researches consider either panoramic or mapping tasks on short videos. In this paper, an automatic mosaicking algorithm is proposed for both mapping and panoramic tasks based on the adapted key-frame on videos of any length. The speeded up robust features (SURF) and the grid motion statistic (GMS) algorithm are used for feature extraction and matching between consecutive frames, which are used to compute the transformation. In order to reduce the influence of the accumulated error during image stitching, an evaluation metric is put forward for the transformation matrix. Besides, a self-growth method is employed to stitch the global image for long videos. The algorithm is evaluated by using aerial-view and panoramic videos respectively on the graphic processing unit (GPU) device, which can satisfy the real-time requirement. The experimental results demonstrate that the proposed algorithm is able to achieve a better performance than the state-of-art.

中文翻译：

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基于动态关键帧的自动视频拼接算法

自动视频拼接是计算机视觉中的一项具有挑战性的任务。当前的研究考虑了短视频的全景或映射任务。在本文中，基于任意长度视频的自适应关键帧，为映射和全景任务提出了一种自动镶嵌算法。加速鲁棒特征（SURF）和网格运动统计（GMS）算法用于特征提取和连续帧之间的匹配，用于计算变换。为了减少图像拼接过程中累积误差的影响，提出了变换矩阵的评价指标。此外，采用自生长方法拼接长视频的全局图像。该算法在图形处理单元（GPU）设备上分别使用鸟瞰和全景视频进行评估，可以满足实时性要求。实验结果表明，所提出的算法能够实现比最先进的算法更好的性能。