Anomaly Detection & Behavior Prediction: Higher-Level Fusion Based on Computational Neuroscientific Principles

Higher-level fusion aims to enhance situational awareness and assessment (Endsley, 1995). Enhancing the understanding analysts/operators derive from fused information is a key objective. Modern systems are capable of fusing information from multiple sensors, often using inhomogeneous modalities, into a single, coherent kinematic track picture. Although this provides a self-consistent representation of considerable data, having hundreds, or possibly thousands, of moving elements depicted on a display does not make for ease of comprehension (even with the best possible human-computer interface design). Automated assistance for operators that supports ready identification of those elements most worthy of their attention is one approach for effectively leveraging lower-level fusion products. A straightforward, commonly employed method is to use rule-based motion analysis techniques. Pre-defined activity patterns can be detected and identified to operators. Detectable patterns range from simple trip-wire crossing or zone penetration to more sophisticated multi-element interactions, such as rendezvous. Despite having a degree of utility, rule-based methods do not provide a complete solution. The complexity of real-world situations arises from the myriad combinations of conditions and contexts that make development of thorough, all-encompassing sets of rules impossible. Furthermore, it is also often the case that the events of interest and/or the conditions and contexts in which they are noteworthy can change at rates for which it is impractical to extend or modify large rule corpora. Also, pre-defined rules cannot assist operators interested in being able to determine whether any unusual activity is occurring in the track picture they are monitoring. Timely identification and assessment of anomalous activity within an area of interest is an increasingly important capability—one that falls under the enhanced situational awareness objective of higher-level fusion. A precursor of being able to automatically notify operators about the presence of anomalous activity is the capability to detect deviations from normal behavior. To do this, a model of normal behavior is required. It is impractical to consider a rule-based approach for achieving such a task, so an adaptive method is required: that is, a capability to learn what is normal in a scene is required. This normalcy representation can then be used to assess new data in order to determine their degree of normalcy and provide notification when any O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg