With today's ubiquitous deployment of video cameras and other edge devices, progress in
edge computing is happening at an incredible speed. Yet, one aspect of real-time video …

Edge computing has been getting a momentum with ever-increasing data at the edge of the
network. In particular, huge amounts of video data and their real-time processing …

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

Real-time analytics on video data demands intensive computation resources and high
energy consumption. Traditional cloud-based video analytics relies on large centralized …

Along the trend pushing computation from the network core to the edge where the most of
data are generated, edge computing has shown its potential in reducing response time …

Live video analytics have become a key technology to support surveillance, security, traffic
control, and even consumer multimedia applications in real time. The continuous growth in …

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 …

Analyzing video feeds from large camera networks requires enormous compute and
bandwidth. Edge computing has been proposed to ease the burden by bringing resources to …

Cloud computing and edge computing models are popularly applied in emerging
applications, such as smart homes, smart parks, and connected autonomous vehicles for …

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 …