To develop smart city and intelligent manufacturing, video cameras are being increasingly
deployed. In order to achieve fast and accurate response to live video queries (eg, license …

With increasing availability and use of Internet of Things (IoT) devices such as sensors and
video cameras, large amounts of streaming data is now being produced at high velocity …

Video analytics pipelines have steadily shifted to edge deployments to reduce bandwidth
overheads and privacy violations, but in doing so, face an ever-growing resource tension …

Real-time video analytics on small autonomous drones poses several difficult challenges at
the intersection of wireless bandwidth, processing capacity, energy consumption, result …

This paper investigates a mobile edge computing environment for video analysis tasks
where edge nodes provide their computation capacities to process the computation …

As video camera deployments continue to grow, the need to process large volumes of real-
time data strains wide-area network infrastructure. When per-camera bandwidth is limited, it …

Video analytics with deep learning techniques has generated immense interest in academia
and industry, captivating minds with its transformative potential. Deep learning techniques …

New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in,
eg, real-time decision-making during disaster incident response to handle the data deluge …

Multimedia Internet-of-Things (IoT) systems have been widely used in surveillance,
automatic behavior analysis and event recognition, which integrate image processing …

Video analytics applications use edge compute servers for processing videos. Compressed
models that are deployed on the edge servers for inference suffer from data drift where the …