Assisted Music-Driven Video Editing

Abstract

Automated video editing has the potential to streamline content creation by intelligently selecting and synchronizing video clips with music. This project presents a video editing assistant that takes raw footage as input, analyzes its visual content using histogram-based scene segmentation, and applies K-Means clustering to identify the most representative clips. The system then aligns selected clips with predefined music segments, ensuring a structured and rhythmically cohesive final edit. By assuming that beat detection is handled externally, the system focuses on optimizing clip selection and sequencing, providing an efficient and adaptable approach to music-driven video editing. This research intends to contribute to the field of automated media production by enhancing creative workflow efficiency while maintaining user control over the editing process.