Challenges and opportunities in DNN-based video analytics: A demonstration of the blazeit video query engine

Daniel Kang, Peter Bailis, Matei Zaharia

Research output: Contribution to conferencePaperpeer-review

Abstract

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 turned to deep learning to perform automatic analyses. However, these methods are: costly (running up to 10x slower than real time, i.e., 3 fps) and cumbersome to deploy, requiring writing complex, imperative code with many low-level libraries (e.g., OpenCV, MXNet). There is an incredible opportunity to leverage techniques from the data management community to automate and optimize these analytics pipelines. In this paper, we describe our ongoing work in the Stanford DAWN lab on BlazeIt, an analytics engine for scalable and usable video analytics that currently contains an optimizing query engine. We propose a demonstration of BlazeIt’s query language, FrameQL, its use cases, and our preliminary work on debugging machine learning, which will show the feasibility of video analytics at scale. We further describe the challenges that arise from large-scale video, progress we have made in automating and optimizing video analytics pipelines, and our plans to extend BlazeIt.

Original languageEnglish (US)
StatePublished - 2019
Externally publishedYes
Event9th Biennial Conference on Innovative Data Systems Research, CIDR 2019 - Pacific Grove, United States
Duration: Jan 13 2019Jan 16 2019

Conference

Conference9th Biennial Conference on Innovative Data Systems Research, CIDR 2019
Country/TerritoryUnited States
CityPacific Grove
Period1/13/191/16/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management
  • Hardware and Architecture

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