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A two-level real-time vision machine combining coarse- and fine-grained parallelism

Lars Jensen, Anders Kjaer-Nielsen, Karl Pauwels, Jeppe Jessen, Marc Van Hulle, Norbert Kruger
The Maersk Mc-Kinney Moller Institute, University of Southern, Denmark, Campusvej 55, 5230 Odense M, Denmark
Journal of Real-Time Image Processing, Volume 5, Number 4, 291-304 (10 June 2010)

@article{jensen2010two,

   title={A two-level real-time vision machine combining coarse-and fine-grained parallelism},

   author={Jensen, L.B.W. and Kj{ae}r-Nielsen, A. and Pauwels, K. and Jessen, J.B. and Van Hulle, M. and Kr{\”u}ger, N.},

   journal={Journal of Real-Time Image Processing},

   pages={1–14},

   issn={1861-8200},

   year={2010},

   publisher={Springer}

}

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In this paper, we describe a real-time vision machine having a stereo camera as input generating visual information on two different levels of abstraction. The system provides visual low-level and mid-level information in terms of dense stereo and optical flow, egomotion, indicating areas with independently moving objects as well as a condensed geometric description of the scene. The system operates at more than 20 Hz using a hybrid architecture consisting of one dual-GPU card and one quad-core CPU. The different processing stages of visual information have rather different characteristics that in some cases make fine-grained parallelization on a GPU less applicable. However, for most of the stages that are not efficiently implementable on a GPU, a coarse parallelization on multiple CPU-cores is applicable. We show that with such hybrid parallelism, we can achieve a speed up of approximately a factor 90 and a reduction of latency of a factor 26 compared to processing on a single CPU-core. Since the vision machine provides generic visual information it can be used in many contexts. Currently it is used in a driver assistance context as well as in two robotic applications.
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