  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.
