  Human pose estimation in two-dimensional images videos has been a hot topic
in the computer vision problem recently due to its vast benefits and potential
applications for improving human life, such as behaviors recognition, motion
capture and augmented reality, training robots, and movement tracking. Many
state-of-the-art methods implemented with Deep Learning have addressed several
challenges and brought tremendous remarkable results in the field of human pose
estimation. Approaches are classified into two kinds: the two-step framework
(top-down approach) and the part-based framework (bottom-up approach). While
the two-step framework first incorporates a person detector and then estimates
the pose within each box independently, detecting all body parts in the image
and associating parts belonging to distinct persons is conducted in the
part-based framework. This paper aims to provide newcomers with an extensive
review of deep learning methods-based 2D images for recognizing the pose of
people, which only focuses on top-down approaches since 2016. The discussion
through this paper presents significant detectors and estimators depending on
mathematical background, the challenges and limitations, benchmark datasets,
evaluation metrics, and comparison between methods.
