Math @ Duke
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Publications [#339989] of Vahid Tarokh
Papers Published
- Banerjee, T; Whipps, G; Gurram, P; Tarokh, V, Cyclostationary statistical models and algorithms for anomaly detection using multi-modal data,
2018 Ieee Global Conference on Signal and Information Processing, Globalsip 2018 Proceedings, vol. abs/1807.06945
(February, 2019),
pp. 126-130 [doi]
(last updated on 2023/06/01)
Abstract: 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.
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