Transform based spatio-temporal descriptors for human action recognition

被引:31
|
作者
Shao, Ling [1 ]
Gao, Ruoyun [2 ]
Liu, Yan [3 ]
Zhang, Hui [4 ,5 ]
机构
[1] Univ Sheffield, Dept Elect & Elect Engn, Sheffield S10 2TN, S Yorkshire, England
[2] Leiden Univ, Dept Comp Sci, NL-2300 RA Leiden, Netherlands
[3] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
[4] United Int Coll, Dept Comp Sci & Technol, Zhuhai, Peoples R China
[5] PKU HKUST Shenzhen Hong Kong Inst, Shenzhen Key Lab Intelligent Media & Speech, Shenzhen, Peoples R China
基金
中国国家自然科学基金;
关键词
Transforms; Feature representation; Human action recognition; Spatio-temporal features; Feature extraction;
D O I
10.1016/j.neucom.2010.11.013
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:962 / 973
页数:12
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