Unusual Activity Detection for Persistent Target Surveillance

摘要

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.