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A Survey of Distributed Computer Vision Algorithms

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Handbook of Ambient Intelligence and Smart Environments

Abstract

Over the past twenty years, the computer vision community has made great strides in the automatic solution to such problems as camera localization and visual tracking. Many algorithms have been made tractable by the rapid increases in computational speed and memory size now available to a single computer. However, the world of visual sensor networks poses several challenges to the direct application of traditional computer vision algorithms. First, visual sensor networks are assumed to contain tens to hundreds of cameras- many more than are considered in many vision applications. Second, these cameras are likely to be spread over a wide geographical area- much larger than the typical computer lab. Third, the cameras are likely to have modest local processors with no ability to communicate beyond a short range.

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Authors and Affiliations

  1. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA

    Richard J. Radke

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  1. Future University Hakodate, Kameda-Nakano 116-2, Hakodate, Hokkaido, 041-8655, Japan

    Hideyuki Nakashima

  2. Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA, 94305-9515, USA

    Hamid Aghajan

  3. School of Computing & Mathematics, University of Ulster at Jordanstown, Shore Road, Newtownabbey, Co. Antrim, UK, BT37 0QB

    Juan Carlos Augusto

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Radke, R.J. (2010). A Survey of Distributed Computer Vision Algorithms. In: Nakashima, H., Aghajan, H., Augusto, J.C. (eds) Handbook of Ambient Intelligence and Smart Environments. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-93808-0_2

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