Deep learning-based video analytics is computation-intensive. Manufacturers such as
Nvidia have launched many embedded deep learning accelerators and are rapidly gaining …

Deep learning algorithms are an essential component of video analytics systems, in which
the content of a video stream is analyzed. Although numerous studies target optimizing …

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 …

In recent years, deep learning models have revolutionized computer vision, enabling
diverse applications. However, these models are computationally expensive, and leveraging …

Deep learning video analytic systems process live video feeds from multiple cameras with
computer vision models deployed on edge or cloud. To optimize utility for these systems …

Applying deep neural networks (DNNs) to video analytics tasks has drawn attention from
both academic and industry communities. However, due to the high computational …

Real-time analytics on video data demands intensive computation resources and high
energy consumption. Traditional cloud-based video analytics relies on large centralized …

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 …

Mobile-edge computing can help enable low-latency and accurate video analytics.
However, it is difficult to make efficient utilization of limited edge resources because of the …

With today's ubiquitous deployment of video cameras and other edge devices, progress in
edge computing is happening at an incredible speed. Yet, one aspect of real-time video …