Multiview video summarization plays a crucial role in abstracting essential information form multiple videos of the same location and time. In this paper, we propose a new approach for the multiview summarization. The proposed approach uses the BIRCH clustering algorithm for the first time on the initial set of frames to get rid of the static and redundant. The work presents a new approach for shot boundary detection using frame similarity measures Jaccard and Dice. The algorithm performs effectively synchronized merging of keyframes from all camera-views to obtain the final summary. Extensive experimentation conducted on various datasets suggests that the proposed approach significantly outperforms most of the existing video summarization approaches. To state a few, a 1.5% improvement on video length reduction, 24.28% improvement in compression ratio, and 6.4% improvement in quality assessment ratio is observed on the lobby dataset.

多视图视频摘要在提取来自相同位置和时间的多个视频的基本信息中起着至关重要的作用。在本文中，我们提出了一种用于多视图摘要的新方法。所提出的方法首次在初始帧集上使用BIRCH聚类算法来摆脱静态和冗余。这项工作提出了一种使用帧相似性度量Jaccard和Dice进行镜头边界检测的新方法。该算法可以有效同步所有摄像机视图中关键帧的合并，以获得最终摘要。在各种数据集上进行的广泛实验表明，所提出的方法明显优于大多数现有的视频摘要方法。仅举几例，视频长度减少了1.5％，提高了24。