TY - CONF
T1 - Challenges and opportunities in DNN-based video analytics
T2 - 9th Biennial Conference on Innovative Data Systems Research, CIDR 2019
AU - Kang, Daniel
AU - Bailis, Peter
AU - Zaharia, Matei
N1 - Funding Information:
This research was supported in part by affiliate members and other supporters of the Stanford DAWN project—Google, Intel, Microsoft, NEC, Teradata, and VMware—as well as DARPA under No. FA8750-17-2-0095 (D3M), industrial gifts and support from Toyota Research Institute, Juniper Networks, Keysight Technologies, Hitachi, Facebook, Northrop Grumman, NetApp, and the NSF under grants DGE-1656518 and CNS-1651570.
Funding Information:
This research was supported in part by affiliate members and other supporters of the Stanford DAWN project?Google, Intel, Microsoft, NEC, Teradata, and VMware?as well as DARPA under No. FA8750-17-2-0095 (D3M), industrial gifts and support from Toyota Research Institute, Juniper Networks, Keysight Technologies, Hitachi, Facebook, Northrop Grumman, NetApp, and the NSF under grants DGE-1656518 and CNS-1651570.
Publisher Copyright:
© 2019 Conference on Innovative Data Systems Research (CIDR). All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
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M3 - Paper
AN - SCOPUS:85084014417
Y2 - 13 January 2019 through 16 January 2019
ER -