The availability of vast video collections and the accuracy of ML models has generated
significant interest in video analytics systems. Since naively processing all frames using …

Advances in deep learning have led to a resurgence of interest in video analytics. In an
exploratory video analytics pipeline, a data scientist often starts by searching for a global …

Video database management systems (VDBMSs) enable automated analysis of videos at
scale using computationally-intensive deep learning models. To reduce the computational …

We introduce EQUI-VOCAL: a new system that automatically synthesizes queries over
videos from limited user interactions. The user only provides a handful of positive and …

We present a new video storage system (VSS) designed to decouple high-level video
operations from the low-level details required to store and efficiently retrieve video data. VSS …

The growth of video volumes and increased DNN capabilities has led to a growing desire for
video analytics. In response, the data analytics community has proposed multiple systems …

Current video database management systems (VDBMSs) fail to support the growing number
of video datasets in diverse domains because these systems assume clean data and rely on …

Social media, self-driving cars, and traffic cameras produce video streams at large scales
and cheap cost. However, storing and querying video at such scales is prohibitively …

Many applications deployed to public clouds are concerned about the confidentiality of their
outsourced data, such as financial services and electronic patient records. A plausible …

Cloud Technology is adopted to process video streams because of the great features
provided to video stream providers such as the high flexibility of using virtual machines and …