An efficient index structure for large-scale geo-tagged video databases

An unprecedented number of user-generated videos (UGVs) are currently being collected by mobile devices, however, such unstructured data are very hard to index and search. Due to recent development, UGVs can be geo-tagged, e.g., GPS locations and compass directions, at the acquisition time at a very fine spatial granularity. Ideally, each video frame can be tagged by the spatial extent of its coverage area, termed Field-Of-View (FOV). In this paper, we focus on the challenges of spatial indexing and querying of FOVs in a large repository. Since FOVs contain both location and orientation information, and their distribution is non-uniform, conventional spatial indexes (e.g., R-tree, Grid) cannot index them efficiently. We propose a class of new R-tree-based index structures that effectively harness FOVs' camera locations, orientations and view-distances, in tandem, for both filtering and optimization. In addition, we present novel search strategies and algorithms for efficient range and directional queries on FOVs utilizing our indexes. Our experiments with a real-world dataset and a large synthetic video dataset (over 30 years worth of videos) demonstrate the scalability and efficiency of our proposed indexes and search algorithms and their superiority over the competitors.