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 will drive a wide range of applications with great potential to impact society.
A geographically distributed architecture of public clouds and edges that extend down to the …

Advanced vision analytics plays a key role in a plethora of real-world applications.
Unfortunately, many of these applications fail to leverage the abundant compute resource in …

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 …

Abstract The NVIDIA AI City Challenge has been created to accelerate intelligent video
analysis that helps make cities smarter and safer. With millions of traffic video cameras …

Emerging deep learning-based video analytics tasks demand computation-intensive neural
networks and powerful computing resources on the cloud to achieve high accuracy. Due to …

Video cameras are pervasively deployed for security and smart city scenarios, with millions
of them in large cities worldwide. Achieving the potential of these cameras requires …

Edge-cloud collaborative video analytics is transforming the way data is being handled,
processed, and transmitted from the ever-growing number of surveillance cameras around …

Advancements in deep neural networks (DNNs) and widespread deployment of video
cameras have fueled the need for video analytics systems. Despite rapid advances in …

We present PANDA, the first gigaPixel-level humAN-centric viDeo dAtaset, for large-scale,
long-term, and multi-object visual analysis. The videos in PANDA were captured by a …