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 …

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 …

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 …

In the future, the video-enabled camera will be the most pervasive type of sensor in the
Internet of Things. Such cameras will enable continuous surveillance through …

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 …

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 …

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

Video analytics using networked smart cameras has become a core function for many
applications including surveillance, object detection, AR/VR, and so on. In recent years, a …

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 …

Recent advances in machine learning and deep learning have enabled many existing
applications in smart cities, autonomous systems, and wearable devices. These applications …