A Novel 3D Gradient LBP Descriptor for Action Recognition

作者：Guo, Zhaoyang^*; Wang, Xin'an; Wang, Bo; Xie, Zheng

来源：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2017, E100D(6): 1388-1392.

DOI：10.1587/transinf.2017EDL8006

• action recognition
• spatio-temporal interest points
• local binary pattern

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.

• 出版日期2017-6
• 单位北京大学深圳研究生院