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An adaptive method for energy-efficiency in battery-powered embedded smart cameras

Authors:

Mauricio Casares,

Senem VelipasalarAuthors Info & Claims

ICDSC '10: Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras

Pages 167 - 174

https://doi.org/10.1145/1865987.1866014

Published: 31 August 2010 Publication History

Abstract

With the introduction of battery-powered wireless embedded smart cameras, it has now become viable to deploy large numbers of spatially-distributed cameras with more flexibility in terms of camera locations. However, many challenges remain to be addressed to build operational, battery-powered, wireless smart-camera networks. Battery life is limited, and video processing tasks, such as foreground detection and tracking, consume considerable amount of energy. Thus, it is essential to design and implement lightweight algorithms and methods to increase the energy efficiency of each camera node, and thus the overall life-time of the camera network. We present an adaptive method based on tracking that significantly decreases the energy consumption of the embedded camera. The microprocessor on the camera board is sent to an idle state depending on the amount of activity in the scene. The amount of time the camera remains in idle mode is adaptively changed based on the speeds of tracked objects. Instead of continuously capturing and processing every frame, the camera drops frames during idle mode while preserving the tracking performance and thus system reliability at the same time. We present experimental results showing the energy-efficiency of the proposed method, and the gain in battery life. The proposed methodology provides 25% to 37% savings in the energy consumption, and 45:83% to 65% increase in the battery life depending on the number of objects in the scene and their speeds.

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Cited By

Casares MVelipasalar S(2018)Adaptive Methodologies for Energy-Efficient Object Detection and Tracking With Battery-Powered Embedded Smart CamerasIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2011.216276221:10(1438-1452)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1109/TCSVT.2011.2162762
Nair SSomani NGrunau ADean-Leon EKnoll A(2018)Image Processing Units on Ultra-low-cost Embedded HardwareJournal of Signal Processing Systems10.1007/s11265-017-1267-190:6(913-929)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1007/s11265-017-1267-1

Index Terms

An adaptive method for energy-efficiency in battery-powered embedded smart cameras
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Computer graphics
    1. Image manipulation
      1. Image processing

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Information

Published In

cover image ACM Conferences

ICDSC '10: Proceedings of the Fourth ACM/IEEE International Conference on Distributed Smart Cameras

August 2010

252 pages

ISBN:9781450303170

DOI:10.1145/1865987

General Chair:
Marilyn Wolf
Georgia Tech
,
Program Chairs:
Gian Luca Foresti
University of Udine, Italy
,
Horst Bischof
Graz University of Technology, Austria

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 August 2010

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Division of Computer and Network Systems

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ICDSC '10

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ICDSC '10: International Conference on Distributed Smart Cameras

August 31 - September 4, 2010

Georgia, Atlanta

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Overall Acceptance Rate 92 of 117 submissions, 79%

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Cited By

Casares MVelipasalar S(2018)Adaptive Methodologies for Energy-Efficient Object Detection and Tracking With Battery-Powered Embedded Smart CamerasIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2011.216276221:10(1438-1452)Online publication date: 31-Dec-2018
https://dl.acm.org/doi/10.1109/TCSVT.2011.2162762
Nair SSomani NGrunau ADean-Leon EKnoll A(2018)Image Processing Units on Ultra-low-cost Embedded HardwareJournal of Signal Processing Systems10.1007/s11265-017-1267-190:6(913-929)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1007/s11265-017-1267-1

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