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 …

Video camera plays a growing important role in advancing industrial control towards a
higher level of automation. Thus, video analytics become highly demanded, especially for …

Smart cameras with on-device deep learning inference capabilities are enabling distributed
video analytics at the data source without sending raw video data over the often unreliable …

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

Cloud computing and edge computing models are popularly applied in emerging
applications, such as smart homes, smart parks, and connected autonomous vehicles for …

Analyzing video feeds from large camera networks requires enormous compute and
bandwidth. Edge computing has been proposed to ease the burden by bringing resources to …

Edge video analytics is becoming the solution to many safety and management tasks. Its
wide deployment, however, must first address the tension between inference accuracy and …

Video cameras are pervasively deployed in city scale for public good or community safety (ie
traffic monitoring or suspected person tracking). However, analyzing large scale video feeds …

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 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 …