Action recognition using vague division DMMs

The Journal of Engineering

Xiaofeng Wang¹  Jun Kong³  Ke Jin³  Min Jiang⁵  Hongtao Huo⁵ 

[1] College of Electrical Engineering, Xinjiang University, Urumqi 830047, People'Department of Information Security Engineering, People'Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, People's Public Security University of China, Beijing 100038, People's Republic of China

关键词: vague boundary;    adjacent sequences;    vague division DMM;    original subsequence;    MSR Action3D dataset;    depth motion map;    VB-sequence;    UTD-MHAD dataset;    depth map sequences;    MSR Action Pairs dataset;    absolute value;    MSR Gesture 3D dataset;    orthogonal Cartesian planes;    robust probabilistic collaborative representation classification;    human action recognition method;    video sequence;   

DOI  :  10.1049/joe.2016.0330

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

This study presents a novel human action recognition method based on the sequences of depth maps, which provide additional body shape and motion information for action recognition. First, the authors divide each depth sequence into a number of sub-sequences. All these sub-sequences are of uniform length. By controlling vague boundary (VB), they construct a VB-sequence which consists of an original sub-sequence and its adjacent sequences. Then, each depth frame in a VB-sequence is projected onto three orthogonal Cartesian planes, and the absolute value of the difference between two consecutive projected maps is accumulated to form a depth motion map (DMM) to describe the dynamic feature of a VB-sequence. Finally, they concatenate the DMMs of all the VB-sequences in one video sequence to describe an action. Collectively, they call them VB division of depth model. For classification, they apply robust probabilistic collaborative representation classification. The recognition results applied to the MSR Action Pairs, MSR Gesture 3D, MSR Action3D, and UTD-MHAD datasets indicate superior performance of their method over most existing methods.

【 授权许可】

CC BY