Real-time deep video analytic at the edge is an enabling technology for emerging
applications, such as vulnerable road user detection for autonomous driving, which requires …

Edge computing has opened new doors for real-time video analytics applications due to its
ability to offer significantly faster response times by processing videos near the source …

The real-time query of surveillance video plays a significant role in many fields such as
public safety, smart city, and abnormality monitoring. However, with the exponential growth …

Powered by deep learning, video analytic applications process millions of camera feeds in
real‐time to extract meaningful information from their surroundings. And this number grows …

With the large number of cameras deployed in smart industrial parks and smart campuses,
edge devices and location-fixed edge servers are deployed near to these cameras and help …

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 …

With the advancement of wireless networks, many mobile applications are becoming
increasingly popular. These applications, which include real-time vision processing …

Along the trend pushing computation from the network core to the edge where the most of
data are generated, edge computing has shown its potential in reducing response time …

Video analytics systems conduct video preprocessing to filter out unnecessary frames and
model inference using appropriately selected neural networks for high analytics speed …

Real-time analytics on video data from mobile devices demands intensive computation
resources for the application like traffic monitoring and anomaly detection. Leveraging edge …