Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/13424
Title: Transform based spatio-temporal descriptors for human action recognition
Authors: Shao, L
Gao, R
Liu, Y 
Zhang, H
Keywords: Feature extraction
Feature representation
Human action recognition
Spatio-temporal features
Transforms
Issue Date: 2011
Publisher: Elsevier
Source: Neurocomputing, 2011, v. 74, no. 6, p. 962-973 How to cite?
Journal: Neurocomputing 
Abstract: Classic transformation methods have been widely and efficiently used in image processing areas, such as image de-noising, image segmentation, feature detection, and compression. Based on their compact signal and image representation ability, we apply the transform based techniques on the video recognition area to extract discriminative information from each given video sequence, and use the transformed coefficients as descriptors for representing and recognizing human actions in video sequences. We validate our proposed methods on the KTH and the Hollywood datasets, which have been extensively studied by a lot of researchers. The proposed descriptors, especially the wavelet transform based descriptor, yield promising results on action recognition.
URI: http://hdl.handle.net/10397/13424
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2010.11.013
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