Human body posture recognition — A survey

Human posture recognition is gaining increasing attention in the field of computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. With the development of image processing and computer vision techniques, it is possible to analysis human behavior automatically by recognition the posture of human body, which has become one of most significant research topic in both computer-based intelligent video surveillance system and pattern recognition area. Paper surveys the use of human body posture mechanism for interaction with the computers, describing various techniques for performing accurate recognition. In this paper different phase of human body posture discuss. Also include the different techniques for each phase of the system.

[1]  Nabil Zerrouki,et al.  Automatic Classification of Human Body Postures Based on the Truncated SVD , 2014 .

[2]  Ismail Khalid Kazmi,et al.  A Survey of 2D and 3D Shape Descriptors , 2013, 2013 10th International Conference Computer Graphics, Imaging and Visualization.

[3]  Qiong Wang,et al.  Human posture recognition based on skeleton data , 2015, 2015 IEEE International Conference on Progress in Informatics and Computing (PIC).

[4]  Hexi Li,et al.  The recognition of moving human body posture based on combined neural network , 2013, IEEE Conference Anthology.

[5]  Mannes Poel,et al.  Comparison of silhouette shape descriptors for example-based human pose recovery , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[6]  Suman K. Mitra,et al.  Human Action Recognition Using DFT , 2011, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics.

[7]  Jia-xin Cai,et al.  Human action recognition in the fractional Fourier domain , 2015, 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR).

[8]  Hyun Gook Kang,et al.  Human body posture recognition with discrete cosine transform , 2016, 2016 International Conference on Big Data and Smart Computing (BigComp).

[9]  Marco Morana,et al.  Human Activity Recognition Process Using 3-D Posture Data , 2015, IEEE Transactions on Human-Machine Systems.

[10]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[11]  Nooritawati Md Tahir,et al.  Modelling of initial reference frame for background subtraction , 2010, 2010 6th International Colloquium on Signal Processing & its Applications.

[12]  Mohan M. Trivedi,et al.  3-D Posture and Gesture Recognition for Interactivity in Smart Spaces , 2012, IEEE Transactions on Industrial Informatics.

[13]  Chia-Feng Juang,et al.  Vision-based human body posture recognition using support vector machines , 2012, 4th International Conference on Awareness Science and Technology.