基于柔性目标图的遮挡保留监控视频摘要

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yongwei Nie, Wei Ge, Siming Zeng, Qing Zhang, Guiqing Li, Ping Li, Hongmin Cai
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引用次数: 0

摘要

视频摘要是一种将长监控视频压缩成短摘要的技术。它面临着处理源视频中原本相互遮挡的对象的挑战。先前的方法要么将遮挡对象作为单个对象处理,但降低了压缩比;或者必须单独分离遮挡对象,但破坏它们之间的相互作用并产生视觉伪影。本文提出了一种新的数据结构,称为柔性目标图(FOG)来处理原始遮挡。我们的基于fog的视频摘要方法可以灵活地操纵每个目标,同时保持它们之间的原始遮挡,在保持目标交互的同时实现高的摘要率。引入FOG带来的一个具有挑战性的问题是,FOG可能包含产生冲突的循环。我们提出了一种循环冲突解决算法来解决这个问题。此外,视频摘要方法通常最小化多目标能量函数。以往的方法是同时优化多个目标,需要在多个目标之间取得平衡。相反,我们提出了一种消耗更少运行时间而产生更高质量的逐步优化策略。实验证明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Occlusion-Preserved Surveillance Video Synopsis with Flexible Object Graph

Video synopsis is a technique that condenses a long surveillance video to a short summary. It faces challenges to process objects originally occluding each other in the source video. Previous approaches either treat occlusion objects as a single object, which however reduce compression ratio; or have to separate occlusion objects individually, but destroy interactions between them and yield visual artifacts. This paper presents a novel data structure called Flexible Object Graph (FOG) to handle original occlusions. Our FOG-based video synopsis approach can manipulate each object flexibly while preserving the original occlusions between them, achieving high synopsis ratio while maintaining interactions of objects. A challenging issue that comes with the introduction of FOG is that FOG may contain circulations that yield conflicts. We solve this problem by proposing a circulation conflict resolving algorithm. Furthermore, video synopsis methods usually minimize a multi-objective energy function. Previous approaches optimize the multiple objectives simultaneously which needs to strike a balance between them. Instead, we propose a stepwise optimization strategy consuming less running time while producing higher quality. Experiments demonstrate the effectiveness of our method.

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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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