The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

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 …

Sprocket is a highly configurable, stage-based, scalable, serverless video processing
framework that exploits intra-video parallelism to achieve low latency. Sprocket enables …

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

Applying deep convolutional neural networks (NN) to video data at scale poses a substantial
systems challenge, as improving inference accuracy often requires a prohibitive cost in …

Video analytics will drive a wide range of applications with great potential to impact society.
A geographically distributed architecture of public clouds and edges that extend down to the …

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

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 …

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 …