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 …

To develop smart city and intelligent manufacturing, video cameras are being increasingly
deployed. In order to achieve fast and accurate response to live video queries (eg, license …

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 analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …

Video analytics constitute a core component of many wireless services that require
processing of voluminous data streams emanating from handheld devices. Multi-Access …

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 …

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 …

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 …