Real-time video analytics services aim to provide users with accurate recognition results
timely. However, existing studies usually fall into the dilemma between reducing delay and …

Real-time video analytics services are expected to deliver accurate recognition results to
users timely. However, existing studies usually fail in the dilemma between reducing delay …

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 …

In this work, we aim to maintain the timeliness of a multipath-based real-time video analytics
system by analyzing the age of processed information (AoPI) at the receiver side. In the …

Edge computing has gained momentum in recent years, and can provide more immediate
analysis of streaming video data. However, the edge devices often lack the computing …

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 …

Edge video analytics, which offloads video stream processing tasks to edge servers, is a key
enabling technology for deep neural networks (DNNs)-based intelligent video applications …

This article introduces a novel distributed model for handling in real-time, edge-based
Artificial Intelligence analytics, such as the ones required for smart video surveillance. The …

As the killer application of edge computing, video analytics typically involves multiple vision
components in the pipeline, which together determine the quality of service (QoS) for users …