Unusual activity detection for persistent target surveillance

The information generated by the integrated sensor suite is massive. Simply bringing the information to the decision makers is a cognitive disaster due to the information overload. Intelligent algorithms need to be developed to exploit and filter the information. In this work, we seek to develop content-level change detection and activity-level information fusion and filtering algorithms to detect unusual activities for persistent target surveillance applications.

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