Please use this identifier to cite or link to this item:
|Title:||Parallelizing convolutional neural networks for action event recognition in surveillance videos|
|Keywords:||Action recognition;Convolutional neural network;Parallelization;MapReduce;Multicore|
|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.|
|Appears in Collections:||Dept of Electronic and Computer Engineering Research Papers|
Items in BURA are protected by copyright, with all rights reserved, unless otherwise indicated.