Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data
Signal Processing
2018-07-19 v1 Machine Learning
Methodology
Machine Learning
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.
Keywords
Cite
@article{arxiv.1807.06945,
title = {Cyclostationary Statistical Models and Algorithms for Anomaly Detection Using Multi-Modal Data},
author = {Taposh Banerjee and Gene Whipps and Prudhvi Gurram and Vahid Tarokh},
journal= {arXiv preprint arXiv:1807.06945},
year = {2018}
}