Efficient Indexing and Querying of Geo-tagged Aerial Videos

Driven by the advances in control engineering, material science and sensor technologies, drones are becoming significantly prevalent in daily life (e.g., event coverage, tourism). Consequently, an unprecedented number of drone videos (or aerial videos) are recorded and consumed. In such a large repository, it is difficult to index and search aerial videos in an unstructured form. However, due to the rich sensor instrumentations of drones, aerial videos can be geotagged (e.g., GPS locations, drone rotation angles) at the acquisition time, providing an opportunity for efficient management of aerial videos by exploiting their corresponding spatial structures. Each aerial video frame can thus be represented as its spatial coverage, termed aerial Field-Of-View (aerial-FOV). This effectively converts a challenging aerial video management problem into a spatial database problem on aerial-FOVs. In this paper, we focus on efficient indexing and querying of aerial-FOVs.