Adaptive spatial scale for cognitively-inspired motion pattern learning & analysis algorithms for higher-level fusion and automated scene understanding

To date, our neurobiologically inspired algorithms for exploiting track data to learn normal patterns of motion behavior, detect deviations from normalcy, and predict future behavior have operated at fixed spatial scales. Although these models continuously adapted to incoming track data through incremental learning in order to adjust to evolving situations, the fundamental spatial scale of the learned models did not change over time. This constraint necessitates a trade-off between model maturation rate and deviation detection or behavior prediction performance. This paper describes updates to our approach that enable data-driven model scale adaptation. Anomaly detection is based on coarse resolution models during early learning stages and progressively switches to finer resolution models as sufficient data are received. This approach increases speed of model maturation with small amounts of data, while improving model fidelity and anomaly detection sensitivity as increasing amounts of data are received. These capabilities contribute to higher-level fusion situational awareness and assessment objectives. They also provide essential elements for automated scene understanding to shift operator focus from sensor monitoring and activity detection to assessment and response. Our learning algorithms learn behavioral patterns at a variety of conceptual, spatial, and temporal levels to reduce a massive amount of track data to a rich set of information regarding their field of regard that supports decision-making and timely response initiation.