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Speeding up a Video Summarization Approach Using GPUs and Multicore CPUs

Suellen S. de Almeida, Antonio Carlos de Nazare Junior, Arnaldo de Albuquerque Arauujo, Guillermo Camara-Chavez, David Menotti
Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
Procedia Computer Science, Volume 29, Pages 159-171, 2014

@article{deAlmeida2014159,

   title={Speeding up a Video Summarization Approach Using GPUs and Multicore CPUs},

   journal={Procedia Computer Science},

   volume={29},

   number={0},

   pages={159 – 171},

   year={2014},

   note={2014 International Conference on Computational Science},

   issn={1877-0509},

   doi={http://dx.doi.org/10.1016/j.procs.2014.05.015},

   url={http://www.sciencedirect.com/science/article/pii/S1877050914001926},

   author={Suellen S. de Almeida and Antonio Carlos de Nazare Junior and Arnaldo de Albuquerque Arauujo and Guillermo Camara-Chavez and David Menotti},

   keywords={Multicore CPUs}

}

The recent progress of digital media has stimulated the creation, storage and distribution of data, such as digital videos, generating a large volume of data and requiring efficient technologies to increase the usability of these data. Video summarization methods generate concise summaries of video contents and enable faster browsing, indexing and accessing of large video collections, however, these methods often perform slow with large duration and high quality video data. One way to reduce this long time of execution is to develop a parallel algorithm, using the advantages of the recent computer architectures that allow high parallelism. This paper introduces parallelizations of a summarization method called VSUMM, targetting either Graphic Processor Units (GPUs) or multicore Central Processor Units (CPUs), and ultimately a sensible distribution of computation steps onto both hardware to maximise performance, called "hybrid". We performed experiments using 180 videos varying frame resolution (320 x 240, 640 x 360, and 1920 x 1080) and video length (1, 3, 5, 10, 20, and 30 minutes). From the results, we observed that the hybrid version reached the best results in terms of execution time, achieving 7x speed up in average.
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