A survey of human motion analysis using depth imagery

被引:260
作者
Chen, Lulu [1 ]
Wei, Hong [1 ]
Ferryman, James [1 ]
机构
[1] Univ Reading, Sch Syst Engn, Computat Vis Grp, Whiteknights Reading RG6 6AY, England
关键词
Range data; Depth sensor; Survey; Human pose estimation; Human action recognition; 3D body model; ACTION RECOGNITION; ACQUISITION; LIGHT;
D O I
10.1016/j.patrec.2013.02.006
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Analysis of human behaviour through visual information has been a highly active research topic in the computer vision community. This was previously achieved via images from a conventional camera, however recently depth sensors have made a new type of data available. This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it. In particular, the Microsoft Kinect has made high-resolution real-time depth cheaply available. The main published research on the use of depth imagery for analysing human activity is reviewed. Much of the existing work focuses on body part detection and pose estimation. A growing research area addresses the recognition of human actions. The publicly available datasets that include depth imagery are listed, as are the software libraries that can acquire it from a sensor. This survey concludes by summarising the current state of work on this topic, and pointing out promising future research directions. For both researchers and practitioners who are familiar with this topic and those who are new to this field, the review will aid in the selection, and development, of algorithms using depth data. C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码:1995 / 2006
页数:12
相关论文
共 78 条
  • [1] Human Activity Analysis: A Review
    Aggarwal, J. K.
    Ryoo, M. S.
    [J]. ACM COMPUTING SURVEYS, 2011, 43 (03)
  • [2] Motion history image: its variants and applications
    Ahad, Md. Atiqur Rahman
    Tan, J. K.
    Kim, H.
    Ishikawa, S.
    [J]. MACHINE VISION AND APPLICATIONS, 2012, 23 (02) : 255 - 281
  • [3] MAINTAINING KNOWLEDGE ABOUT TEMPORAL INTERVALS
    ALLEN, JF
    [J]. COMMUNICATIONS OF THE ACM, 1983, 26 (11) : 832 - 843
  • [4] Anguelov D, 2005, PROC CVPR IEEE, P169
  • [5] [Anonymous], IEEE T PATTERN ANAL
  • [6] [Anonymous], TECHNICAL REPORT
  • [7] [Anonymous], 2012, 2012 IEEE INT C ROB
  • [8] [Anonymous], 2010, 2010 IEEE GLOBAL TEL
  • [9] [Anonymous], 2012, 2012 IEEE COMP SOC C, DOI DOI 10.1109/CVPRW.2012.6239179
  • [10] [Anonymous], TECHNICAL REPORT