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

Mobile Object Tracking in Panoramic Video and LiDAR for Radiological Source-Object Attribution and Improved Source Detection