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Accelerating Video Analytics

Published: 31 January 2022 Publication History

Abstract

MOTIVATION. The advent of inexpensive, high-quality cameras has led to a rapid increase in the volume of generated video data [19, 16]. It is now feasible to automatically analyze these video datasets at scale due to two developments over the last decade. First, researchers have designed complex, computationally-intensive deep learning (DL) models that capture the contents of a given set of video frames (e.g., objects present in a particular frame [11]) [15]. Second, the computational capabilities of hardware accelerators for evaluating these DL models have increased over the last decade (e.g., TPUs) [8]. We anticipate that automated analysis of videos will reduce the labor cost of analyzing video

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  • (2024)COUPLE: Orchestrating Video Analytics on Heterogeneous Mobile Processors2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00128(1561-1574)Online publication date: 13-May-2024

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cover image ACM SIGMOD Record
ACM SIGMOD Record  Volume 50, Issue 4
December 2021
48 pages
ISSN:0163-5808
DOI:10.1145/3516431
Issue’s Table of Contents
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 January 2022
Published in SIGMOD Volume 50, Issue 4

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  • (2024)COUPLE: Orchestrating Video Analytics on Heterogeneous Mobile Processors2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00128(1561-1574)Online publication date: 13-May-2024

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