Mobile-edge computing can help enable low-latency and accurate video analytics.
However, it is difficult to make efficient utilization of limited edge resources because of the …

Low-latency and accuracy-guaranteed video analytics is essential to many delay-sensitive
camera-based applications. Analyzing video frames on edge nodes in proximity can …

Deep learning-based video analytics is computation-intensive. Manufacturers such as
Nvidia have launched many embedded deep learning accelerators and are rapidly gaining …

Mobile edge computing (MEC), as a key ingredient of the 5G ecosystem, is envisioned to
support demanding applications with stringent latency requirements. The basic idea is to …

Real-time video analytics typically require video frames to be processed by a query to
identify objects or activities of interest while adhering to an end-to-end frame processing …

With the advancements in Machine Learning (ML) and edge computing, increasing efforts
have been devoted to edge video analytics. However, most of the existing works fail to …

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 …

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

Real-time analytics on video data demands intensive computation resources and high
energy consumption. Traditional cloud-based video analytics relies on large centralized …

The vast adoption of mobile devices with cameras has greatly contributed to the proliferation
of the creation and distribution of videos. For a variety of purposes, valuable information may …