Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor
Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor
复制标题
异常子轨迹的在线检测：基于共形异常检测和局部离群因子的滑动窗口方法
DOI：
10.1007/978-3-642-33412-2_20
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
G. Falkman
中科院分区：
文献类型：
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作者：
Rikard Laxhammar;G. Falkman
异常轨迹的自动检测是监控领域的一个重要问题。针对这个问题，已经提出了基于正常轨迹模式学习的各种算法。然而，这些算法存在以下一个或多个限制：首先，它们本质上是为数据库中的离线异常检测而设计的。其次，它们对局部子轨迹异常不敏感。第三，它们涉及许多参数的调整，并且可能会出现较高的误报率。本文的主要贡献是滑动窗口局部异常值共形异常检测器（SWLO-CAD）的提出和讨论，这是一种用于在线检测局部子轨迹异常的算法。它是先前提出的共形异常检测器的一个实例，因此可以以经过良好校准的误报率在线运行。此外，SWLO-CAD 基于局部异常值因子，这是先前提出的对局部异常敏感的异常值度量。因此，SWLO-CAD 具有一组独特的属性来解决上述问题。
Automated detection of anomalous trajectories is an important problem in the surveillance domain. Various algorithms based on learning of normal trajectory patterns have been proposed for this problem. Yet, these algorithms suffer from one or more of the following limitations: First, they are essentially designed for offline anomaly detection in databases. Second, they are insensitive to local sub-trajectory anomalies. Third, they involve tuning of many parameters and may suffer from high false alarm rates. The main contribution of this paper is the proposal and discussion of the Sliding Window Local Outlier Conformal Anomaly Detector (SWLO-CAD), which is an algorithm for online detection of local sub-trajectory anomalies. It is an instance of the previously proposed Conformal anomaly detector and, hence, operates online with well-calibrated false alarm rate. Moreover, SWLO-CAD is based on Local outlier factor, which is a previously proposed outlier measure that is sensitive to local anomalies. Thus, SWLO-CAD has a unique set of properties that address the issues above.