Organizations deploy a hierarchy of clusters-cameras, private clusters, public clouds-for
analyzing live video feeds from their cameras. Video analytics queries have many …

Video cameras are pervasively deployed for security and smart city scenarios, with millions
of them in large cities worldwide. Achieving the potential of these cameras requires …

Camera deployments are ubiquitous, but existing methods to analyze video feeds do not
scale and are error-prone. We describe Optasia, a dataflow system that employs relational …

Large volumes of videos are continuously recorded from cameras deployed for traffic control
and surveillance with the goal of answering “after the fact” queries: identify video frames with …

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

Video cameras have been deployed at scale today. Driven by the breakthrough in deep
learning (DL), organizations that have deployed these cameras start to use DL-based …

Video cameras are pervasively deployed in city scale for public good or community safety (ie
traffic monitoring or suspected person tracking). However, analyzing large scale video feeds …

Cameras are deployed at scale with the purpose of searching and tracking objects of
interest (eg, a suspected person) through the camera network on live videos. Such cross …

To cope with the high resource (network and compute) demands of real-time video analytics
pipelines, recent systems have relied on frame filtering. However, filtering has typically been …

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 …