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Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data by Banerjee, Taposh; Whipps, Gene; Gurram, Prudhvi; Tarokh, Vahid is a scholarly article available to read on EtoBox.

What is Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data about?

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.

Author

Banerjee, Taposh; Whipps, Gene; Gurram, Prudhvi; Tarokh, Vahid

Published

2018

Language

EN