Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34637
Title: Realistic human action recognition With multimodal feature selection and fusion
Authors: Wu, Q
Wang, Z
Deng, F
Chi, Z 
Feng, DD
Issue Date: 2013
Publisher: IEEE
Source: IEEE transactions on systems, man, and cybernetics : systems, 2013, v. 43, no. 4, p. 875-885 How to cite?
Journal: IEEE transactions on systems, man, and cybernetics : systems
Abstract: Although promising results have been achieved for human action recognition under well-controlled conditions, it is very challenging to recognize human actions in realistic scenarios due to increased difficulties such as dynamic backgrounds. In this paper, we propose to take multimodal (i.e., audiovisual) characteristics of realistic human action videos into account in human action recognition for the first time, since, in realistic scenarios, audio signals accompanying an action generally provide a cue to the nature of the action, such as phone ringing to answering the phone . In order to cope with diverse audio cues of an action in realistic scenarios, we propose to identify effective features from a large number of audio features with the generalized multiple kernel learning algorithm. The widely used space-time interest point descriptors are utilized as visual features, and a support vector machine is employed for both audio- and video-based classifications. At the final stage, fuzzy integral is utilized to fuse recognition results of both audio and visual modalities. Experimental results on the challenging Hollywood-2 Human Action data set demonstrate that the proposed approach is able to achieve better recognition performance improvement than that of integrating scene context. It is also discovered how audio context influences realistic action recognition from our comprehensive experiments.
URI: http://hdl.handle.net/10397/34637
ISSN: 2168-2216 (print)
2168-2232 (electronic)
DOI: 10.1109/TSMCA.2012.2226575
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

WEB OF SCIENCETM
Citations

11
Last Week
0
Last month
Citations as of Feb 21, 2017

Page view(s)

11
Last Week
1
Last month
Checked on Feb 19, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.