The addition of contextual sensors to mobile radiation sensors provides
valuable information about radiological source encounters that can assist in
adjudication of alarms. This study explores how computer-vision based object
detection and tracking analyses can be used to augment radiological data from a
mobile detector system. We study how contextual information (streaming video
and LiDAR) can be used to associate dynamic pedestrians or vehicles with
radiological alarms to enhance both situational awareness and detection
sensitivity. Possible source encounters were staged in a mock urban environment
where participants included pedestrians and vehicles moving in the vicinity of
an intersection. Data was collected with a vehicle equipped with 6 NaI(Tl) 2
inch times 4 inch times 16 inch detectors in a hexagonal arrangement and
multiple cameras, LiDARs, and an IMU. Physics-based models that describe the
expected count rates from tracked objects are used to correlate vehicle and/or
pedestrian trajectories to measured count-rate data through the use of Poisson
maximum likelihood estimation and to discern between source-carrying and
non-source-carrying objects. In this work, we demonstrate the capabilities of
our source-object attribution approach as applied to a mobile detection system
in the presence of moving sources to improve both detection sensitivity and
situational awareness in a mock urban environment