Quickest Event Detection Using Multimodal Data In Nonstationary Environments
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Theory and algorithms are developed for event detection using multimodal data in nonstationary environments. The type of nonstationary behavior studied in the paper is one where the statistical properties of the data are periodic in nature. The periodic pattern of the observation process is modeled using independent and periodically identically distributed processes, a new class of stochastic processes proposed by us. Algorithms are developed that are minimax asymptotically optimal as the false alarm rate goes to zero. The theory and algorithms are inspired by real multimodal data collected around a 5K run in New York City, but also has applications in anomaly detection in cyber-physical systems and biology, where periodic statistical behavior has been observed. The developed algorithms are applied to sequences of counts of objects and sub-events extracted from images and social media posts in the NYC data.
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