FiGO: Fine-Grained Query Optimization in Video Analytics
Jiashen Cao, Karan Sarkar, Ramyad Hadidi, Joy James Prabhu Arulraj, Hyesoon Kim · Proceedings of the 2022 International Conference on Management of Data · 2022
Video database management systems (VDBMSs) enable automated analysis of videos at scale using computationally-intensive deep learning models. To reduce the computational overhead of these models, researchers have proposed two techniques: (1) leveraging a specialized, lightweight model to filter out irrelevant frames or to directly answer the query, and (2) using a cascade of models of increasing complexity to answer the query. For both techniques, the query optimizer generates a coarse-grained query plan for the entire video. These techniques suffer from four limitations: (1) lower query accuracy over hard-to-detect predicates, (2) lower filtering efficacy with frequently-occurring objects, (3) lower accuracy due to nontrivial model cascade configuration, and (4) missed optimization opportunities due to coarse-grained planning for the entire video.