Yang Yoonsik, Kim Haksub, Choi Heeseung, Chae Seungho, Kim Ig-Jae
IEEE Trans Image Process. 2021;30:8318-8331. doi: 10.1109/TIP.2021.3114986. Epub 2021 Oct 5.
Visual surveillance produces a significant amount of raw video data that can be time consuming to browse and analyze. In this work, we present a video synopsis methodology called "scene adaptive online video synopsis via dynamic tube rearrangement using octree (SSOcT)" that can effectively condense input surveillance videos. Our method entailed summarizing the input video by analyzing scene characteristics and determining an effective spatio-temporal 3D structure for video synopsis. For this purpose, we first analyzed the attributes of each extracted tube with respect to scene geometry and complexity. Then, we adaptively grouped the tubes using an online grouping algorithm that exploits these scene characteristics. Finally, the tube groups were dynamically rearranged using the proposed octree-based algorithm that efficiently inserted and refined tubes containing high spatio-temporal movements in real time. Extensive video synopsis experimental results are provided, demonstrating the effectiveness and efficiency of our method in summarizing real-world surveillance videos with diverse scene characteristics.
视觉监控会产生大量原始视频数据，浏览和分析这些数据可能会很耗时。在这项工作中，我们提出了一种名为“通过使用八叉树的动态管重排实现场景自适应在线视频概要（SSOcT）”的视频概要方法，该方法可以有效地压缩输入的监控视频。我们的方法包括通过分析场景特征并确定用于视频概要的有效时空三维结构来总结输入视频。为此，我们首先针对场景几何形状和复杂性分析每个提取管的属性。然后，我们使用一种利用这些场景特征的在线分组算法对管进行自适应分组。最后，使用所提出的基于八叉树的算法对管组进行动态重排，该算法能够实时有效地插入和优化包含高时空运动的管。提供了大量视频概要实验结果，证明了我们的方法在总结具有不同场景特征现实世界监控视频方面的有效性和效率。
