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 Computer Engineering Research Papers

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