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Advances in Embedded Computer Vision pp 249–264Cite as

Data-Driven Stream Mining Systems for Computer Vision

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

In this chapter, we discuss the state of the art and future challenges in adaptive stream mining systems for computer vision. Adaptive stream mining in this context involves the extraction of knowledge from image and video streams in real-time, and from sources that are possibly distributed and heterogeneous. With advances in sensor and digital processing technologies, we are able to deploy networks involving large numbers of cameras that acquire increasing volumes of image data for diverse applications in monitoring and surveillance. However, to exploit the potential of such extensive networks for image acquisition, important challenges must be addressed in efficient communication and analysis of such data under constraints on power consumption, communication bandwidth, and end-to-end latency. We discuss these challenges in this chapter, and we also discuss important directions for research in addressing such challenges using dynamic, data-driven methodologies.

Keywords

  • Stream Mining System
  • Dynamic Data Driven Application Systems (DDDAS)
  • Embedded Computer Vision
  • Wireless Local Area Network (WLAN)
  • State Value Function

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Acknowledgments

This work is supported by the US Air Force Office of Scientific Research under the DDDAS Program.

Author information

Authors and Affiliations

  1. University of Maryland, College Park, MD, USA

    Shuvra S. Bhattacharyya & Kishan Sudusinghe

  2. University of California, Los Angeles, CA, USA

    Mihaela van der Schaar, Onur Atan & Cem Tekin

Authors
  1. Shuvra S. Bhattacharyya
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  2. Mihaela van der Schaar
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  3. Onur Atan
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  4. Cem Tekin
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  5. Kishan Sudusinghe
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Corresponding author

Correspondence to Shuvra S. Bhattacharyya .

Editor information

Editors and Affiliations

  1. Interphase Corporation, Plano, Texas, USA

    Branislav Kisačanin

  2. Vienna University of Technology, Vienna, Austria

    Margrit Gelautz

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Bhattacharyya, S.S., van der Schaar, M., Atan, O., Tekin, C., Sudusinghe, K. (2014). Data-Driven Stream Mining Systems for Computer Vision. In: Kisačanin, B., Gelautz, M. (eds) Advances in Embedded Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-09387-1_12

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  • DOI: https://doi.org/10.1007/978-3-319-09387-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09386-4

  • Online ISBN: 978-3-319-09387-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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