Recent advances in neural networks (NNs) have enabled automatic querying of large
volumes of video data with high accuracy. While these deep NNs can produce accurate …

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 …

Large volumes of videos are continuously recorded from cameras deployed for traffic control
and surveillance with the goal of answering “after the fact” queries: identify video frames with …

Recent advances in video processing utilizing deep learning primitives achieved
breakthroughs in fundamental problems in video analysis such as frame classification and …

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

Recent advances in computer vision-in the form of deep neural networks-have made it
possible to query increasing volumes of video data with high accuracy. However, neural …

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 …

Capturing and processing video is increasingly common as cameras become cheaper to
deploy. At the same time, rich video-understanding methods have progressed greatly in the …

We present VStore, a data store for supporting fast, resource-efficient analytics over large
archival videos. VStore manages video ingestion, storage, retrieval, and consumption. It …

Deep convolutional neural networks (CNNs) achieve state-of-the-art accuracy for many
computer vision tasks. But using them for video monitoring applications incurs high …