Many recent video applicationsÐincluding trafficmonitoring, drone analytics, autonomous driving, and virtual realityÐrequire piecing together, combining, and operating overmany related video streams. Despite the massive data volumes involved and the need to jointly reason (both spatially and temporally) about these videos, current techniques to store and manipulate such data are often limited to file systems and simple video processing frameworks that reason about a single video in isolation. We present Apperception, a new type of database management system optimized for geospatial video applications. Apperception comes with an easy to use data model to reason about multiple geospatial video data streams, and a programming interface for developers to collectively reason about the entities observed in those videos. Our demo will let users write queries over video using Apperception and retrieve (in real-time) both metadata and rendered video data. Users can also compare results and observe speedups achieved by using Apperception. PVLDB Reference Format: Yongming Ge, Vanessa Lin, Maureen Daum, Brandon Haynes, Alvin Cheung, and Magdalena Balazinska. Demonstration of Apperception: A Database Management System for Geospatial Video Data. PVLDB, 14(12):