核施設における通常の操作からの盗取の識別
概要
According to the IAEA's Incident and Trafficking Database (ITDB) [1], nuclear security-related incidents occur approximately every three days in the world, with the majority being nuclear and radioactive material theft. Generally, material accountancy is applied as a countermeasure against theft. In this study, a deep learning-based approach was proposed to identify theft of nuclear and radioactive material. As various acts can be performed at the site where nuclear material is handled, the recognition of objects and actions in surveillance images were integrated to perform behavioral analysis. Furthermore, to arrange the detection unit acts in time series data for malicious identification was proposed. The proposed identification framework contains the following elemental techniques: I. Real-time unit acts detection, and II. Real-time malicious acts identification
