Trusted AI Frameworks for Change and Anomaly Detection in Observed ISR Patterns

This project seeks to automate the identification of higher order patterns in ISR (Intelligence, Surveillance and Reconnaissance) sensed detections along with establishing normalcy. The intention is for significant changes from normalcy – anomalies – to be reported to operators as alerts requiring human assessment, decision, and action. In addition, the rationale of the alerts will also be computed and presented in a transparent way to instil user confidence in the results.

Two novel aspects for this project are: (1) the use of multiple strategies for pattern detection, including deep learning and advanced statistical modelling (e.g., Bayesian Computation); and (2) the incorporation of a Pattern Question Answering (PQA) capability to enable intuitive interaction and interrogation of the reported patterns for their rationale. PQA will build on and generalise existing capabilities in Visual Question Answering (VQA) in the fields of Artificial Intelligence and Machine Learning.

Specific application domains will be considered to support the development and demonstration of capability, including domains such as maritime traffic, space situational patterns, and land use patterns.


Project Leader:
Matthew Roughan, The University of Adelaide