Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/13494
Title: | Parallelizing convolutional neural networks for action event recognition in surveillance videos |
Authors: | Wang, Q Zhao, J Gong, D Shen, Y Li, M Lei, Y |
Keywords: | Action recognition;Convolutional neural network;Parallelization;MapReduce;Multicore |
Issue Date: | 2016 |
Publisher: | Springer |
Citation: | International Journal of Parallel Programming, pp. 1 - 26, (2016) |
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. |
URI: | http://bura.brunel.ac.uk/handle/2438/13494 |
DOI: | http://dx.doi.org/10.1007/s10766-016-0451-4 |
ISSN: | 0885-7458 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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