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Author Gu, Feng ♦ Flórez-Revuelta, Francisco ♦ Monekosso, Dorothy ♦ Remagnino, Paolo
Source World Health Organization (WHO)-Global Index Medicus
Content type Text
Publisher Multidisciplinary Digital Publishing Institute
File Format HTM / HTML
Language English
Difficulty Level Medium
Subject Domain (in DDC) Technology ♦ Medicine & health
Abstract Multi-view action recognition has gained a great interest in video surveillance, human computer interaction, and multimedia retrieval, where multiple cameras of different types are deployed to provide a complementary field of views. Fusion of multiple camera views evidently leads to more robust decisions on both tracking multiple targets and analysing complex human activities, especially where there are occlusions. In this paper, we incorporate the marginalised stacked denoising autoencoders (mSDA) algorithm to further improve the bag of words (BoWs) representation in terms of robustness and usefulness for multi-view action recognition. The resulting representations are fed into three simple fusion strategies as well as a multiple kernel learning algorithm at the classification stage. Based on the internal evaluation, the codebook size of BoWs and the number of layers of mSDA may not significantly affect recognition performance. According to results on three multi-view benchmark datasets, the proposed framework improves recognition performance across all three datasets and outputs record recognition performance, beating the state-of-art algorithms in the literature. It is also capable of performing real-time action recognition at a frame rate ranging from 33 to 45, which could be further improved by using more powerful machines in future applications.
Description Country affiliation: United kingdom
Author Affiliation: Gu F ( School of Computing and Information Systems, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK. F.Gu@kingston.ac.uk.); Flórez-Revuelta F ( Faculty of Science, Engineering and Computing, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK. F.Florez@kingston.ac.uk.); Monekosso D ( School of Computing and Information Systems, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK. D.Monekosso@kingston.ac.uk.); Remagnino P ( School of Computing and Information Systems, Kingston University, Penrhyn Road, Kingston upon Thames KT1 2EE, UK. P.Remagnino@kingston.ac.uk.)
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Reading ♦ Research ♦ Self Learning
Interactivity Type Expositive
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-07-16
Publisher Place Switzerland
e-ISSN 14248220
Journal Sensors
Volume Number 15
Issue Number 7


Source: WHO-Global Index Medicus