Activity Based Intelligence (ABI) is the derivation of information from the composite of a series of individual
actions being recorded over a period of time. Due to its temporal nature, ABI is usually developed from
Motion Imagery (MI) or Full Motion Video (FMV) taken from a given scene. One common misconception,
is that ABI boils down to a simple resolution problem; more pixels at a higher frame rate is better. As part
of this research an experiment was designed and performed to address this assumption; by analyzing varying
temporal resolutions in conjunction with several modalities, a trade space for characterizing activities can
be developed. Thermal Infrared (IR), multispectral, and polarimetric data were used to augment RGB MI.
As these data are still being analyzed, this paper gives an update to the experiment and analysis process.
Activity Based Intelligence (ABI) is the derivation of information from the composite of a series of individual actions being recorded over a period of time. Due to its temporal nature, ABI is usually developed from Motion Imagery (MI) or Full Motion Video (FMV) taken of a given scene. One of today's common issues is sifting through such large volumes of temporal data. Here we propose using a technique known as tipping an cueing to alleviate the need to manually sift through said data. Being able to tip the analysts or automated algorithm towards a particular person or object in the data is useful in reducing search time. We propose using a polarimetric sensor to identify objects of interest, in a scene where their signature would be unusual. Once identified, this data will be used to cue a FMV RGB sensor to track the object and determine the activities being executed by the person bringing the object into the scene.
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