用于异常检测的时空视频解析
Spatio-temporal Video Parsing for Abnormality Detection
摘要：
由于所有不规则对象和行为的类的无限大小，视频中的异常检测提出了特别的挑战。因此，没有（或远远不够）异常训练样本可用，我们需要在测试数据中发现异常，而实际上不知道它们是什么。然而，该领域的流行概念是直接搜索独立于另一个的单个异常局部补丁或图像区域。为了解决这个问题，我们提出了一种时空视频解析的视频异常联合检测方法。视频解析的目标是找到一组不可或缺的正常时空对象假设，这些假设共同解释视频的所有前景，同时得到正常训练样本的支持。因此，我们避免直接检测异常，并间接发现它们作为那些假设，这些假设是覆盖前景所需的，而不是用正常样本来解释它们。在图形模型中，通过MAP推理将abbands本地化，并通过将其制定为凸优化问题来有效地解决它。我们通过实验评估了我们的方法在几个具有挑战性的基准集，改善了所有标准基准的最先进的异常分类和本地化方面。
英文摘要：
Abnormality detection in video poses particular challenges due to the infinite size of the class of all irregular objects and behaviors. Thus no (or by far not enough) abnormal training samples are available and we need to find abnormalities in test data without actually knowing what they are. Nevertheless, the prevailing concept of the field is to directly search for individual abnormal local patches or image regions independent of another. To address this problem, we propose a method for joint detection of abnormalities in videos by spatio-temporal video parsing. The goal of video parsing is to find a set of indispensable normal spatio-temporal object hypotheses that jointly explain all the foreground of a video, while, at the same time, being supported by normal training samples. Consequently, we avoid a direct detection of abnormalities and discover them indirectly as those hypotheses which are needed for covering the foreground without finding an explanation for themselves by normal samples. Abnormalities are localized by MAP inference in a graphical model and we solve it efficiently by formulating it as a convex optimization problem. We experimentally evaluate our approach on several challenging benchmark sets, improving over the state-of-the-art on all standard benchmarks both in terms of abnormality classification and localization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
