Nowadays, video cameras are deployed in large scale for spatial monitoring of physical
places (eg, surveillance systems in the context of smart cities). The massive camera …

Today, video cameras are deployed in dense for monitoring physical places eg, city,
industrial, or agricultural sites. In the current systems, each camera node sends its feed to a …

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 …

Video cameras are becoming ubiquitous in our daily lives. With the recent advancement of
Artificial Intelligence (AI), live video analytics are enabling various useful services, including …

The increasing complexity of deep learning and massive deployment of cameras at the edge
have drastically increased the resource demand of edge data analytics. Compared to …

The proliferation of edge devices has pushed computing from the cloud to the data sources,
and video analytics is among the most promising applications of edge computing. Running …

Cameras are deployed at scale with the purpose of searching and tracking objects of
interest (eg, a suspected person) through the camera network on live videos. Such cross …

Video cameras have been deployed at scale today. Driven by the breakthrough in deep
learning (DL), organizations that have deployed these cameras start to use DL-based …

Enterprises are increasingly deploying large camera networks for video analytics. Many
target applications entail a common problem template: searching for and tracking an object …

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 …