Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data

Published

Journal Article

© 2018 IEEE. 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.

Full Text

Duke Authors

Cited Authors

  • Banerjee, T; Whipps, G; Gurram, P; Tarokh, V

Published Date

  • February 20, 2019

Published In

  • 2018 Ieee Global Conference on Signal and Information Processing, Globalsip 2018 Proceedings

Start / End Page

  • 126 - 130

Digital Object Identifier (DOI)

  • 10.1109/GlobalSIP.2018.8646417

Citation Source

  • Scopus