TY  - JOUR
AU  - Wenjuan Gong
AU  - Xuena Zhang
AU  - Jordi Gonzalez
AU  - Andrews Sobral
AU  - Thierry Bouwmans
AU  - Changhe Tu
AU  - El-hadi Zahzah
PY  - 2016//
TI  - Human Pose Estimation from Monocular Images: A Comprehensive Survey
T2  - SENS
JO  - Sensors
SP  - 1966
VL  - 16
IS  - 12
KW  - human pose estimation
KW  - human bodymodels
KW  - generativemethods
KW  - discriminativemethods
KW  - top-down methods
KW  - bottom-up methods
N2  - Human pose estimation refers to the estimation of the location of body parts and how they are connected in an image. Human pose estimation from monocular images has wide applications (e.g., image indexing). Several surveys on human pose estimation can be found in the literature, but they focus on a certain category; for example, model-based approaches or human motion analysis, etc. As far as we know, an overall review of this problem domain has yet to be provided. Furthermore, recent advancements based on deep learning have brought novel algorithms for this problem. In this paper, a comprehensive survey of human pose estimation from monocular images is carried out including milestone works and recent advancements. Based on one standard pipeline for the solution of computer vision problems, this survey splits the problem into several modules: feature extraction and description, human body models, and modelingmethods. Problem modeling methods are approached based on two means of categorization in this survey. One way to categorize includes top-down and bottom-up methods, and another way includes generative and discriminative methods. Considering the fact that one direct application of human pose estimation is to provide initialization for automatic video surveillance, there are additional sections for motion-related methods in all modules: motion features, motion models, and motion-based methods. Finally, the paper also collects 26 publicly available data sets for validation and provides error measurement methods that are frequently used.
UR  - https://doi.org/10.3390/s16121966
L1  - http://refbase.cvc.uab.es/files/GZG2016.pdf
UR  - http://dx.doi.org/10.3390/s16121966
N1  - ISE; 600.098; 600.119
ID  - Wenjuan Gong2016
ER  -