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 …

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 …

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 …

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 streaming and computer games are among the most popular and highest bandwidth
consuming media in the Internet. Video contents consume around 70% of the total …

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 …

The efficient processing of video streams is a key component in many emerging Internet of
Things (IoT) and edge applications, such as Virtual and Augmented Reality (V/AR) and self …

Now we are entering the era of the Internet of Everything (IoE) and billions of sensors and
actuators are connected to the network. As one of the most sophisticated IoE applications …

With the installation of enormous public safety and transportation infrastructure cameras,
video analytics has come to play an essential part in public safety. Typically, video analytics …