Social media, self-driving cars, and traffic cameras produce video streams at large scales
and cheap cost. However, storing and querying video at such scales is prohibitively …

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 streaming is growing in popularity and has become the most bandwidth-consuming
Internet service. As such, robust streaming in terms of low latency and uninterrupted …

Utilizing big data processing platform to analyze and extract insights from unstructured video
streams becomes emerging trend in video surveillance area. As the first step, how to …

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 …

Organizations deploy a hierarchy of clusters-cameras, private clusters, public clouds-for
analyzing live video feeds from their cameras. Video analytics queries have many …

The proliferation of camera-enabled devices and large video repositories has led to a
diverse set of video analytics applications. These applications rely on video pipelines …

With the exponential growth of video data, there is a pressing need for efficient video
analysis technology. Modern query frameworks aim to accelerate queries by reducing the …

Video sharing (eg, YouTube, Vimeo, Facebook, TikTok) accounts for the majority of internet
traffic, and video processing is also foundational to several other key workloads (video …