As video camera deployments continue to grow, the need to process large volumes of real-
time data strains wide-area network infrastructure. When per-camera bandwidth is limited, it …

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 …

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 …

With increasing availability and use of Internet of Things (IoT) devices such as sensors and
video cameras, large amounts of streaming data is now being produced at high velocity …

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 …

The world is witnessing an unprecedented increase in camera deployment. The USA and
UK, for instance, have one camera for every 8 people. Video analytics from these cameras …

Major cities worldwide have millions of cameras deployed for surveillance, business
intelligence, traffic control, crime prevention, etc. Real-time analytics on video data demands …

Deep learning with Deep Neural Networks (DNNs) can achieve much higher accuracy on
many computer vision tasks than classic machine learning algorithms. Because of the high …

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 …

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 …