Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics

Neil Agarwal, Ravi Netravali

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Commercial retrospective video analytics platforms have increasingly adopted general interfaces to support the custom queries and convolutional neural networks (CNNs) that different applications require. However, existing optimizations were designed for settings where CNNs were platform- (not user-) determined, and fail to meet at least one of the following key platform goals when that condition is violated: reliable accuracy, low latency, and minimal wasted work. We present Boggart, a system that simultaneously meets all three goals while supporting the generality that today's platforms seek. Prior to queries being issued, Boggart carefully employs traditional computer vision algorithms to generate indices that are imprecise, but are fundamentally comprehensive across different CNNs/queries. For each issued query, Boggart employs new techniques to quickly characterize the imprecision of its index, and sparingly run CNNs (and propagate results to other frames) in a way that bounds accuracy drops. Our results highlight that Boggart's improved generality comes at low cost, with speedups that match (and most often, exceed) prior, model-specific approaches.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
PublisherUSENIX Association
Pages933-951
Number of pages19
ISBN (Electronic)9781939133335
StatePublished - 2023
Event20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023 - Boston, United States
Duration: Apr 17 2023Apr 19 2023

Publication series

NameProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023

Conference

Conference20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
Country/TerritoryUnited States
CityBoston
Period4/17/234/19/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering

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