Action Recognition Using 3D Histograms Of Texture And A Multi-Class Boosting Classifier

Keywords

Action recognition; boosting classifier; depth image; multi-class classification; texture feature

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.

Publication Date

10-1-2017

Publication Title

IEEE Transactions on Image Processing

Volume

26

Issue

10

Number of Pages

4648-4660

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/TIP.2017.2718189

Socpus ID

85021832532 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85021832532

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