With the vigorous development of artificial intelligence (AI), intelligence applications based
on deep neural networks (DNNs) have changed people's lifestyles and production …

The development of object detection has led to huge improvements in human interaction
systems. Object detection is a challenging task because it involves many parameters …

The Korean Institute of Communications and Information Sciences (KICS), which is the
largest information and communication technology institute in Korea, has been active in …

Major cities worldwide have millions of cameras deployed for surveillance, business
intelligence, traffic control, crime prevention, etc. Real-time analytics on video data demands …

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 …

Deep learning–based video analytics demands high network bandwidth to ferry the large
volume of data when deployed on the cloud. When incorporated at the edge side, only …

With the proliferation of video surveillance system deployment and related applications, real-
time video analysis is very critical to achieving intelligent monitoring, autonomous driving …

Edge intelligence (EI) becomes a trend to push the deep learning frontiers to the network
edge, so that deep neural networks (DNNs) applications can be well leveraged at resource …

Edge computing has been designed to deploy resources in the proximity of IoT devices,
which reduces latency and network overhead. Nevertheless, resources on edge servers are …

As deep learning technology becomes advanced, mobile vision applications, such as
augmented reality (AR) or autonomous vehicles, are prevalent. The performance of such …