Towards Intelligent Video Understanding Applied to Plasma Facing Component Monitoring

In this paper, we promote intelligent plasma facing component video monitoring for both real-time purposes (machine protection issues) and post event analysis purposes (plasma-wall interaction understanding). We propose a vision-based system able to automatically detect and classify into different pre-defined categories thermal phenomena such as localized hot spots or transient thermal events (e.g. electrical arcing) from infrared imaging data of PFCs. This original computer vision system is made intelligent by endowing it with highlevel reasoning (i.e. integration of a priori knowledge of thermal even t spatiotemporal properties to guide the recognition), self-adaptability to varying conditions (e.g. different ther mal scenes and plasma scenarios), and learning capabilities (e.g. statistical modelling of event behaviour based o n training samples). Copyright line will be provided by the publisher