CYCLOSTATIONARY STATISTICAL MODELS AND ALGORITHMS FOR ANOMALY DETECTION USING MULTI-MODAL DATA
CYCLOSTATIONARY STATISTICAL MODELS AND ALGORITHMS FOR ANOMALY DETECTION USING MULTI-MODAL DATA
复制标题
使用多模态数据进行异常检测的循环平稳统计模型和算法
DOI：
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发表时间：
2018
期刊：
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通讯作者：
V. Tarokh
中科院分区：
文献类型：
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作者：
T. Banerjee;Gene T. Whipps;Prudhvi K. Gurram;V. Tarokh
提出了一种多模态数据异常检测框架。采用基于深度神经网络的对象检测器来从数据中提取对象和子事件的计数。提出了一种循环平稳模型来模拟计数序列中的行为规律。异常检测问题被公式化为一个问题，检测学习循环平稳行为的偏差。序列算法提出了使用所提出的模型来检测异常。所提出的算法被证明是渐近有效的，在一个明确的意义。将开发的算法应用于由CCTV图像和社交媒体帖子组成的多模态数据，以检测纽约市的5K跑步。
A framework is proposed to detect anomalies in multi-modal data. A deep neural network-based object detector is employed to extract counts of objects and sub-events from the data. A cyclostationary model is proposed to model regular patterns of behavior in the count sequences. The anomaly detection problem is formulated as a problem of detecting deviations from learned cyclostationary behavior. Sequential algorithms are proposed to detect anomalies using the proposed model. The proposed algorithms are shown to be asymptotically efficient in a well-defined sense. The developed algorithms are applied to a multi-modal data consisting of CCTV imagery and social media posts to detect a 5K run in New York City.