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 …

In this paper, we investigate the optimal configuration and dependence-aware task
assignment for multi-task edge video analytics. Multi-task video analytics involves multiple …

Numerous edge applications that rely on video analytics demand precise, low-latency
processing of multiple video streams from cameras. When these cameras are mobile, such …

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 …

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

Deploying deep convolutional neural network (CNN) to perform video analytics at edge
poses a substantial system challenge, as running CNN inference incurs a prohibitive cost in …

The surging penetration of video cameras drives the rapid growth of video frames processed
on the mobile edge. However, the scarce bandwidth and limited edge computing resources …

With the proposal of edge computing, lots of intelligence applications have made significant
progress. For enormous video analysis, how to further accelerate the process is still a major …

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 …

With the easy access to mobile networks and the proliferation of video applications, video
traffic is occupying a great portion of the network traffic, which poses a new challenge of how …