%0 Conference Paper
%A Hoang, Tieu Binh
%A Ma, Thi Chau
%A Akihiro, Sugimoto
%A Bui, The Duy
%B 2018 the 7th International Conference on Computer and Communication Engineering (ICCCE)
%C Malaysia
%D 2018
%F SisLab:3123
%T Selecting active frames for action recognition with 3D convolutional network
%U https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3123/
%X Recent applications of Convolutional Neural Networks, especially 3-Dimensional Convoltutional Neural Networks (3DCNNs) for human action recognition  (HAR) in videos have widely used. In this paper, we  use a multi-stream framework which is a combination  from separated networks with different kind of input  generated from unique video dataset. To achieve the  high results, firstly, we proposed a method to extract  the active frames (called Selected Active Frames -  SAF) from a videos to build datasets for 3DCNNs in  video classifying problem. Second, we deploy a new  approach called Vote fusion which considered as an  effective fusion method for ensembling multi-stream  networks. From the various datasets generated from  videos, we extract frames by our method and feed  into 3DCNNs for feature extraction, then we carry out  training and then fuse the results of softmax layers  of these streams. We evaluate the proposed methods  on solving action recognition problem. These method  are carried on three well-known datasets (HMFB51,  UCF101, and KTH). The results are also compared to  the state-of-the-art results to illustrate the efficiency  and effectiveness in our approach