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

Video database management systems (VDBMSs) enable automated analysis of videos at
scale using computationally-intensive deep learning models. To reduce the computational …

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

Advances in deep learning have led to a resurgence of interest in video analytics. In an
exploratory video analytics pipeline, a data scientist often starts by searching for a global …

As video volumes grow, analysts are increasingly able to query the real world. Since
manually watching these growing volumes of video is infeasible, analysts have increasingly …

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 …

Recently, video database management systems (VDBMSs) have re-emerged as an active
area of research and development. To accelerate innovation in this area, we present Visual …

State-of-the-art video database management systems (VDBMSs) often use lightweight proxy
models to accelerate object retrieval and aggregate queries. The key assumption underlying …

To cope with the high resource (network and compute) demands of real-time video analytics
pipelines, recent systems have relied on frame filtering. However, filtering has typically been …

Camera deployments are ubiquitous, but existing methods to analyze video feeds do not
scale and are error-prone. We describe Optasia, a dataflow system that employs relational …