This thesis addresses the challenge of generating long-duration, high-quality videos using the XL30 FEG/SFEG/SIRION Scanning Electron Microscope (SEM), a task traditionally hindered by slow acquisition speeds, sample drift, and the absence of automated frame scheduling. As a result, researchers lack practical tools to observe nanoscale dynamics over extended experiments. The goal of the project for this subsystem was to develop an image-processing pipeline capable of stabilising SEM recordings and enabling automated video creation from sequential still images. To achieve this, software was developed to process frames acquired at user-defined intervals, correct drift, and assemble images into a cohesive video. Drift was mitigated through a dual-layer strategy: small displacements were compensated using purely software based frame stabilisation techniques, while larger misalignments were addressed by calculating beam shift vectors to support mechanical correction in collaboration with the SEM Control subsystem. The drift between two images was calculated using a phase correlation based algorithm. The resulting prototype successfully stabilised long image sequences and produced smooth, high-definition SEM videos, even in the presence of significant sample drift. These outcomes demonstrate the feasibility of automated SEM video generation and provide a modular framework that can be extended with improved robustness for challenging imaging conditions.