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Surveillance Computing

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Introduction to Intelligent Surveillance

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Abstract

Modern computing has to face the hardware bottleneck at present. Nowadays, supercomputing including multi-thread, multi-core, GPU, and FPGA technologies (Kilts, Advanced FPGA design. Wiley, Hoboken, 2007; Stallings, Operating systems internals and design principals. Pearson Education Limited, 2015) are alleged for resolving the problems and overcoming the technical barriers (Sanders and Kandrot, CUDA by examples: an introduction to general-purpose GPU programming. Addison-Wesley, Upper Saddle River, 2011). In this chapter, we will dwell on how to use these cutting-edge technologies in digital surveillance and make intelligent computing much faster.

There are several criteria in evaluating or choosing parallel programming models (Rauber and Runger, Parallel programming for multicore and cluster systems. Springer, Berlin 2010); a few well-known parallel programming models such as OpenMP, UPC, CUDA, etc. have been adopted in practice (Sanders and Kandrot, CUDA by examples: an introduction to general-purpose GPU programming. Addison-Wesley, Upper Saddle River, 2011).

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Authors and Affiliations

  1. Department of Computer Science, Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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  1. Wei Qi Yan
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Yan, W.Q. (2017). Surveillance Computing. In: Introduction to Intelligent Surveillance. Springer, Cham. https://doi.org/10.1007/978-3-319-60228-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-60228-8_9

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