A Novel 3D Gradient LBP Descriptor for Action Recognition

Zhaoyang GUO  Xin'an WANG  Bo WANG  Zheng XIE  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.6   pp.1388-1392
Publication Date: 2017/06/01
Publicized: 2017/03/02
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8006
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
Keyword: 
action recognition,  spatio-temporal interest points,  local binary pattern,  

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Summary: 
In the field of action recognition, Spatio-Temporal Interest Points (STIPs)-based features have shown high efficiency and robustness. However, most of state-of-the-art work to describe STIPs, they typically focus on 2-dimensions (2D) images, which ignore information in 3D spatio-temporal space. Besides, the compact representation of descriptors should be considered due to the costs of storage and computational time. In this paper, a novel local descriptor named 3D Gradient LBP is proposed, which extends the traditional descriptor Local Binary Patterns (LBP) into 3D spatio-temporal space. The proposed descriptor takes advantage of the neighbourhood information of cuboids in three dimensions, which accounts for its excellent descriptive power for the distribution of grey-level space. Experiments on three challenging datasets (KTH, Weizmann and UT Interaction) validate the effectiveness of our approach in the recognition of human actions.