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 …

In smart city scenarios, the huge proliferation of monitoring cameras scattered in public
spaces has posed many challenges to network and processing infrastructure. A few dozen …

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 …

With the development of artificial intelligence (AI) techniques and the increasing popularity
of camera-equipped devices, many edge video analytics applications are emerging, calling …

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 …

Widely deployed smart cameras are generating a large amount of video data and capable of
processing frames on devices. Empowered by edge computing, the video data can also be …

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 …

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 …

Owing to the massive amount of video data being generated as a result of high proliferation
of surveillance cameras, the manpower to monitor such system is relatively expensive …

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 …