The edge-cloud collaboration architecture can support Deep Neural Network-based (DNN)
video analytics with low inference delays and high accuracy. However, the video analytics …

Deep learning video analytic systems process live video feeds from multiple cameras with
computer vision models deployed on edge or cloud. To optimize utility for these systems …

Deep neural network (DNN) inference poses unique challenges in serving computational
requests due to high request intensity, concurrent multi-user scenarios, and diverse …

Multi-access Edge Cloud (MEC) networks are powerful for providing emerging computation-
intensive and latency-sensitive applications with low latency leveraging ubiquitous edge …

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 …

Deep Neural Network (DNN)-based video analytics significantly improves recognition
accuracy in computer vision applications. Deploying DNN models at edge nodes, closer 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 …

In order to schedule resources efficiently or maintain applications' continuity for mobile
customers, edge platforms often need to adaptively migrate the applications on them …

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 …

Multi-camera systems are now widely employed across numerous domains. The
exponential growth of deep learning has simplified the implementation of advanced video …