UC Berkeley

The detection and localization of radiological and/or nuclear material remains a key challenge in homeland security, especially in urban environments. In an effort to improve detection and localization capabilities, networked detector systems can be deployed in urban environments to aid in the detection and localization of radiological and/or nuclear material. However, effectively responding to and interpreting a radiological alarm using spectroscopic data alone may be hampered by a lack of situational awareness, particularly in complex environments. This work investigates the use of LiDAR and streaming video to enable real-time object detection and tracking, and the fusion of this tracking information with radiological data for the purposes of enhanced situational awareness and increased detection sensitivity. This work presents an object detection, tracking, and novel source-object attribution analysis that is capable of operating in real-time. The analysis pipeline is implemented on a custom-developed system that comprises a static 2 in. × 4 in. × 16 in. NaI(Tl) detector co-located with a 64-beam LiDAR and 4 monocular cameras. Using this analysis approach on the static system, 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, the source-object attribution approach is explained in detail and a quantitative performance assessment that characterizes the source-object attribution capabilities of both video and LiDAR is presented. Additionally, experimental results from a mock urban environment are shown using the contextual-radiological data fusion methodology. With this data, the ability to simultaneously track pedestrians and vehicles in a mock urban environment is demonstrated, and using this tracking information both detection sensitivity and situational awareness is improved.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 on a mobile detector system. Using these analyses on a mobile

system, this work studies 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. To perform this study, data was collected 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 in.×4 in.×16 in. detectors in a hexagonal arrangement and multiple cameras and LiDARs as well as an IMU and an INS. In this work, the source-object attribution approach as applied to a mobile system with multiple detectors in the presence of static and moving sources is demonstrated. The results show improved situational awareness and detection sensitivity using video and LiDAR-based trajectories. In addition, it is seen that LiDAR data produces more reliable position estimates of an object compared to using video data, which enables more effective object tracking and attribution, especially in scenarios with vehicle speeds of about 20 mph. Furthermore, with both video and LiDAR data, improved detection sensitivity is demonstrated using an optimal configuration of detectors within a detector array compared to the summed response of a detector array in a mock urban environment. Finally, by correlating vehicle and/or pedestrian trajectories to measured count-rate data, the source is inherently localized to an object, which enables a new paradigm for source localization and might reduce the complexity in detector array design. To test this concept, source-object attribution is performed using different detector array configurations with varying levels of complexity to understand the impact of the angular response on radiological source localization when tracking information is available. The findings demonstrate that using the source-object attribution analysis approach can enable simpler detector array designs to perform source localization in the investigated scenarios.