  Human Pose Estimation (HPE) is one of the fundamental problems in computer
vision. It has applications ranging from virtual reality, human behavior
analysis, video surveillance, anomaly detection, self-driving to medical
assistance. The main objective of HPE is to obtain the person's posture from
the given input. Among different paradigms for HPE, one paradigm is called
bottom-up multi-person pose estimation. In the bottom-up approach, initially,
all the key points of the targets are detected, and later in the optimization
stage, the detected key points are associated with the corresponding targets.
This review paper discussed the recent advancements in bottom-up approaches for
the HPE and listed the possible high-quality datasets used to train the models.
Additionally, a discussion of the prominent bottom-up approaches and their
quantitative results on the standard performance matrices are given. Finally,
the limitations of the existing methods are highlighted, and guidelines of the
future research directions are given.
