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http://bura.brunel.ac.uk/handle/2438/13494
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DC Field | Value | Language |
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dc.contributor.author | Wang, Q | - |
dc.contributor.author | Zhao, J | - |
dc.contributor.author | Gong, D | - |
dc.contributor.author | Shen, Y | - |
dc.contributor.author | Li, M | - |
dc.contributor.author | Lei, Y | - |
dc.date.accessioned | 2016-11-16T13:10:00Z | - |
dc.date.available | 2016-08-06 | - |
dc.date.available | 2016-11-16T13:10:00Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | International Journal of Parallel Programming, pp. 1 - 26, (2016) | en_US |
dc.identifier.issn | 0885-7458 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/13494 | - |
dc.description.abstract | In order to deal with action recognition for large scale video data, this paper presents a MapReduce based parallel algorithm for SASTCNN, a sparse auto-combination spatio-temporal convolutional neural network. We design and implement a parallel matrix multiplication algorithm based on MapReduce. We use the MapReduce programming model to parallelize SASTCNN on a Hadoop platform. In order to take advantage of the computing power of multi-core CPU, the Map and Reduce processes of MapReduce are implemented using a multi-thread technique. A series of experiments on both WEIZMAN and KTH data sets are carried out. Compared with traditional serial algorithms, the feasibility, stability and correctness of the parallel SASTCNN are validated and a speedup in computation is obtained. Experimental results also show that the proposed method could provide more competitive results on the two data sets than other benchmark methods. | en_US |
dc.description.sponsorship | This work was partially supported by NSFC under contract No. 61501451, by the scholarship from China Scholarship Council (CSC) under the Grant CSC No. 201606315022, and by the XMU-NU Joint Strategic Partnership Fund. | en_US |
dc.format.extent | 1 - 26 | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.subject | Action recognition | en_US |
dc.subject | Convolutional neural network | en_US |
dc.subject | Parallelization | en_US |
dc.subject | MapReduce | en_US |
dc.subject | Multicore | en_US |
dc.title | Parallelizing convolutional neural networks for action event recognition in surveillance videos | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s10766-016-0451-4 | - |
dc.relation.isPartOf | International Journal of Parallel Programming | - |
pubs.publication-status | Accepted | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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