Video analytics with Deep Neural Networks (DNNs) empowers many vision-based
applications. However, deploying DNN models for video analytics services must address the …

Deep Neural Network (DNN)-based video analytics significantly improves recognition
accuracy in computer vision applications. Deploying DNN models at edge nodes, closer to …

Driven by plummeting camera prices and advances of video inference algorithms, video
cameras are deployed ubiquitously and organizations usually rely on live video analytics to …

According to YouTube statistics [1], more than 400 hours of content is uploaded to its
platform every minute. At this rate, it is estimated that it would take more than 70 years of …

Mobile video analytics applications, such as smart driving, VR/AR, and video surveillance,
have become increasingly popular due to the proliferation of mobile devices. These …

Real-time video processing in the home, with the benefits of low latency and strong privacy
guarantees, enables virtual reality (VR) applications, augmented reality (AR) applications …

The concept of the Internet of Things (IoT) is a reality now. This paradigm shift has caught
everyones attention in a large class of applications, including IoT-based video analytics …

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 …

The concept of Internet of Things (IoT) is a reality now. This paradigm shift has caught
everyone's attention in a large class of applications, including IoT-based video analytics …

For IoT networks, signals are captured by edge sensors, system-wide decisions are made at
a central host, and processing may be performed at either or both sides of the network …