V2V: Efficiently Synthesizing Video Results for Video Queries

Querying video data has become increasingly popular and useful. Video queries can be complex, ranging from retrieval tasks (“find me the top videos that have … ”), to analytics (“how many videos contained object X per day?”), to excerpting tasks (“highlight and zoom into scenes with object X near object Y”), or combinations thereof. Results for video queries are still typically shown as either relational data or a primitive collection of clickable thumbnails on a web page. Presenting query results in this form is an impedance mismatch with the video medium: they are cumbersome to skim through and are in a different modality and information density compared to the source data. We describe V2V, a system to efficiently synthesize video results for video queries. V2V returns a fully-edited video, allowing the user to consume results in the same manner as the source videos. A key challenge is that synthesizing video results from a collection of videos is computationally intensive, especially within interactive query response times. To address this, V2V features a grammar to express video transformations in a declarative manner and a heuristic optimizer that improves the efficiency of V2V processing in a manner similar to how databases execute relational queries. Experiments show that our V2V optimizer enables video synthesis to run 3x faster.