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Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics

Authors:

Arthi Padmanabhan,

Guoqing Harry Xu,

Ravi NetravaliAuthors Info & Claims

SIGCOMM '20: Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication

Pages 359 - 376

https://doi.org/10.1145/3387514.3405874

Published: 30 July 2020 Publication History

Abstract

To cope with the high resource (network and compute) demands of real-time video analytics pipelines, recent systems have relied on frame filtering. However, filtering has typically been done with neural networks running on edge/backend servers that are expensive to operate. This paper investigates on-camera filtering, which moves filtering to the beginning of the pipeline. Unfortunately, we find that commodity cameras have limited compute resources that only permit filtering via frame differencing based on low-level video features. Used incorrectly, such techniques can lead to unacceptable drops in query accuracy. To overcome this, we built Reducto, a system that dynamically adapts filtering decisions according to the time-varying correlation between feature type, filtering threshold, query accuracy, and video content. Experiments with a variety of videos and queries show that Reducto achieves significant (51-97% of frames) filtering benefits, while consistently meeting the desired accuracy.

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This video presents Reducto, a system for filtering frames on cameras to make real-time video analytics more resource-efficient.

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Cited By

Liang YZhang SWu J(2025)Scrava: Super Resolution-Based Bandwidth-Efficient Cross-Camera Video AnalyticsIEEE Transactions on Mobile Computing10.1109/TMC.2024.346187924:1(293-305)Online publication date: Jan-2025
https://doi.org/10.1109/TMC.2024.3461879
Yi JAcer UKawsar FMin C(2025)Argus: Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart CamerasIEEE Transactions on Mobile Computing10.1109/TMC.2024.345940924:1(117-134)Online publication date: Jan-2025
https://doi.org/10.1109/TMC.2024.3459409
Xiao KGao YLi FXu WChen PDong W(2024)ChatCam: Embracing LLMs for Contextual Chatting-to-Camera with Interest-Oriented Video SummarizationProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997318:4(1-34)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699731
Show More Cited By

Index Terms

Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object detection
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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SIGCOMM '20: Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication

July 2020

814 pages

ISBN:9781450379557

DOI:10.1145/3387514

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Cited By

Liang YZhang SWu J(2025)Scrava: Super Resolution-Based Bandwidth-Efficient Cross-Camera Video AnalyticsIEEE Transactions on Mobile Computing10.1109/TMC.2024.346187924:1(293-305)Online publication date: Jan-2025
https://doi.org/10.1109/TMC.2024.3461879
Yi JAcer UKawsar FMin C(2025)Argus: Enabling Cross-Camera Collaboration for Video Analytics on Distributed Smart CamerasIEEE Transactions on Mobile Computing10.1109/TMC.2024.345940924:1(117-134)Online publication date: Jan-2025
https://doi.org/10.1109/TMC.2024.3459409
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