异常检测
豪斯多夫距离
计算机科学
异常(物理)
恒虚警率
共形异常
k-最近邻算法
豪斯多夫空间
探测器
预警系统
假警报
人工智能
共形映射
数据挖掘
模式识别(心理学)
算法
数学
共形对称
物理
离散数学
数学分析
电信
凝聚态物理
作者
Xinlong Pan,Haipeng Wang,Xueqi Cheng,Xuan Peng,Yong He
标识
DOI:10.1016/j.inffus.2019.12.009
摘要
In the surveillance domain, timely detection of anomaly behaviors is very important and is a great challenge to human operators due to information overload, fatigue and inattention. Many anomaly detection algorithms based on trajectories have been proposed for this problem. However, these algorithms generally have problems such as complex parameter setting, unfaithful statistical model, not well-calibrated false alarm rate, poor ability of online learning and sequential anomaly detection, etc. The theory of conformal prediction was introduced to solve these problems by constructing the sequential Hausdorff nearest neighbor conformal anomaly detector. Yet, it only considers position information of the targets and is not sensitive to velocity and course anomaly behaviors. And the run times are increasing as the increase of the data size, which is not appropriate for early warning surveillance application. In order to solve these problems, sequential multi-factor Hausdorff nearest neighbor conformal anomaly detector (SMFHNNCAD) and sequential multi-factor Hausdorff nearest neighbor inductive conformal anomaly detector (SMFHNNICAD) based on multidimensional trajectories are proposed in this paper. Experiments in both simulated military scenario and realistic civilian scenario show the presented algorithm has a good performance to online detect anomaly behaviors and would have a wide prospect in early warning surveillance systems.
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