Action Recognition Using 3D Histograms of Texture and A Multi-Class Boosting Classifier

Baochang Zhang, Yun Yang, Chen Chen, Linlin Yang, Jungong Han, Ling Shao

Research output: Contribution to journalArticlepeer-review

111 Citations (Scopus)
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Abstract

Human action recognition is an important yet challenging task. This paper presents a low-cost descriptor called 3D histograms of texture (3DHoTs) to extract discriminant features from a sequence of depth maps. 3DHoTs are derived from projecting depth frames onto three orthogonal Cartesian planes, i.e., the frontal, side, and top planes, and thus compactly characterize the salient information of a specific action, on which texture features are calculated to represent the action. Besides this fast feature descriptor, a new multi-class boosting classifier (MBC) is also proposed to efficiently exploit different kinds of features in a unified framework for action classification. Compared with the existing boosting frameworks, we add a new multi-class constraint into the objective function, which helps to maintain a better margin distribution by maximizing the mean of margin, whereas still minimizing the variance of margin. Experiments on the MSRAction3D, MSRGesture3D, MSRActivity3D, and UTD-MHAD data sets demonstrate that the proposed system combining 3DHoTs and MBC is superior to the state of the art.
Original languageEnglish
Pages (from-to)4648-4660
Number of pages13
JournalIEEE Transactions on Image Processing
Volume26
Issue number10
Early online date21 Jun 2017
DOIs
Publication statusPublished - Oct 2017

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