In the field of automated home video editing, exploring the dependence relations between who (character) and where (scene) makes great sense to end-users for content selection. However, such techniques have not been well developed in real applications due to their computational intensity. The emerging multi-core architectures provide an opportunity to speed up those compute expensive algorithms if shift from serial thinking to parallelism. This demonstration presents a scalable parallel system for home video editing. In a realtime processing speed, the system analyzes how many characters and scenes are captured and provides end-users with flexible preference customization. Through kernel module optimization and data-level parallelization, evaluations on a real 8-core machine indicates a near linear speed up could be achieved along with the increasing number of cores.