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Parallelization Techniques for Spatial-Temporal Occupancy Maps from Multiple Video Streams

  • Nathan DeBardeleben
  • Adam Hoover
  • William Jones
  • Walter Ligon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1800)

Abstract

We describe and analyze several techniques to parallelize a novel algorithm that fuses intensity data from multiple video cameras to create a spatial-temporal occupancy map. Instead of tracking objects, the algorithm operates by recognizing free space. The brevity of operations in the algorithm allows a dense spatial occupancy map to be temporally computed at real-time video rates. Since each input image pixel is processed independently, we demonstrate parallel implementations that achieve nearly ideal speedup on a four processor shared memory architecture. Potential applications include surveillance, robotics, virtual reality, and manufacturing environments.

Keywords

Lookup Table Parallelization Technique Multiprocessor Architecture Processor Workload Input Image Pixel 
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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References

  1. 1.
    A. Hoover and B. Olsen, “A Real-Time Occupancy Map from Multiple Video Streams”, in IEEE IGRA, 1999, pp. 2261–2266.Google Scholar
  2. 2.
    D. Patterson and J. Hennessy, Computer Architecture: A Quantitative Approach, second edition, Morgan Kaufmann, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Nathan DeBardeleben
    • 1
  • Adam Hoover
    • 1
  • William Jones
    • 1
  • Walter Ligon
    • 1
  1. 1.Parallel Architecture Research LaboratoryClemson UniversityClemson

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