With the advent of the Internet of Everything, the proliferation of data has put a huge burden
on data centers and network bandwidth. To ease the pressure on data centers, edge …

Machine Learning (ML) has demonstrated great promise in various fields, eg, self-driving,
smart city, which are fundamentally altering the way individuals and organizations live, work …

To cope with the high resource (network and compute) demands of real-time video analytics
pipelines, recent systems have relied on frame filtering. However, filtering has typically been …

As mobile devices continuously generate streams of images and videos, a new class of
mobile deep vision applications are rapidly emerging, which usually involve running deep …

Public edge platforms have drawn increasing attention from both academia and industry. In
this study, we perform a first-of-its-kind measurement study on a leading public edge …

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 …

Edge-cloud collaborative video analytics is transforming the way data is being handled,
processed, and transmitted from the ever-growing number of surveillance cameras around …

Recent advances in neural networks (NNs) have enabled automatic querying of large
volumes of video data with high accuracy. While these deep NNs can produce accurate …

Mez is a novel publish-subscribe messaging system for latency sensitive multi-camera
machine vision applications at the IoT Edge. The unlicensed wireless communication in IoT …

Emerging deep learning-based video analytics tasks demand computation-intensive neural
networks and powerful computing resources on the cloud to achieve high accuracy. Due to …