OTIF: Efficient Tracker Pre-processing over Large Video Datasets

Performing analytics tasks over large-scale video datasets is increasingly common in a wide range of applications, from traffic planning to sports analytics. These tasks generally involve object detection and tracking operations that require pre-processing the video through expensive machine learning models. To address this cost, several video query optimizers have recently been proposed. Broadly, these methods trade large reductions in pre-processing cost for increases in query execution cost: during query execution, they apply query-specific machine learning operations over portions of the video dataset. Although video query optimizers reduce the overall cost of executing a single query over large video datasets compared to naive object tracking methods, executing several queries over the same video remains cost-prohibitive; moreover, the high per-query latency makes these systems unsuitable for exploratory analytics where fast response times are crucial.